diff --git a/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md b/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
index c364495b..de6f7713 100644
--- a/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
+++ b/docs/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:05 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:45 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
diff --git a/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md b/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
index 1848e7f3..5c8725d7 100644
--- a/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
+++ b/docs/recommendations/0acd117521ef5aafb09fed02ab415523b330b058.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:15 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:51 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,8 +74,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
     
         <tr id="None">
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
     
@@ -135,7 +135,7 @@ hide:
           <td>2017-12-01</td>
           <td>2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)</td>
           <td>12</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md b/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
index 8fc4a638..8c86caeb 100644
--- a/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
+++ b/docs/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:36 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:10 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2023-10-24</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>9</td>
           <td>50</td>
         </tr>
     
@@ -109,7 +109,7 @@ hide:
             Hongyuan Yu, Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, A. Liu
           </td>
           <td>2022-07-01</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>44</td>
           <td>35</td>
         </tr>
diff --git a/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md b/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
index e5074a6b..e92846a4 100644
--- a/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
+++ b/docs/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:37 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:12 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -44,13 +44,13 @@ hide:
     
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
           <td>
             Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>69</td>
           <td>18</td>
         </tr>
     
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2024-06-22</td>
           <td>ArXiv</td>
-          <td>11</td>
+          <td>13</td>
           <td>3</td>
         </tr>
     
@@ -97,8 +97,8 @@ hide:
             Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long
           </td>
           <td>2024-02-04</td>
-          <td>DBLP, ArXiv</td>
-          <td>18</td>
+          <td>ArXiv, DBLP</td>
+          <td>21</td>
           <td>67</td>
         </tr>
     
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2023-10-05</td>
           <td>ArXiv</td>
-          <td>57</td>
+          <td>60</td>
           <td>5</td>
         </tr>
     
@@ -134,7 +134,7 @@ hide:
           </td>
           <td>2024-02-16</td>
           <td>ArXiv</td>
-          <td>4</td>
+          <td>5</td>
           <td>8</td>
         </tr>
     
@@ -146,7 +146,7 @@ hide:
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>184</td>
+          <td>195</td>
           <td>9</td>
         </tr>
     
diff --git a/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md b/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
index d9244ef1..34a6a6be 100644
--- a/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
+++ b/docs/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:41 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:18 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -62,7 +62,7 @@ hide:
           </td>
           <td>2024-02-16</td>
           <td>ArXiv</td>
-          <td>4</td>
+          <td>5</td>
           <td>8</td>
         </tr>
     
@@ -74,7 +74,7 @@ hide:
           </td>
           <td>2024-02-25</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>11</td>
           <td>8</td>
         </tr>
     
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2022-09-20</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>77</td>
+          <td>80</td>
           <td>17</td>
         </tr>
     
@@ -134,7 +134,7 @@ hide:
           </td>
           <td>2023-08-16</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>22</td>
           <td>3</td>
         </tr>
     
@@ -146,7 +146,7 @@ hide:
           </td>
           <td>2024-09-17</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>1</td>
         </tr>
     
diff --git a/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md b/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
index 9adaee24..13d0161f 100644
--- a/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
+++ b/docs/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:38 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:13 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -61,8 +61,8 @@ hide:
             Ali Behrouz, Farnoosh Hashemi
           </td>
           <td>2024-02-13</td>
-          <td>DBLP, ArXiv</td>
-          <td>37</td>
+          <td>ArXiv, DBLP</td>
+          <td>39</td>
           <td>9</td>
         </tr>
     
diff --git a/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md b/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
index def5ed2d..d2250fd8 100644
--- a/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
+++ b/docs/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:46 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:28 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -49,7 +49,7 @@ hide:
             Andreas Doerr, Christian Daniel, Martin Schiegg, D. Nguyen-Tuong, S. Schaal, Marc Toussaint, Sebastian Trimpe
           </td>
           <td>2018-01-31</td>
-          <td>DBLP, MAG, ArXiv</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>113</td>
           <td>93</td>
         </tr>
@@ -66,6 +66,18 @@ hide:
           <td>40</td>
         </tr>
     
+        <tr id="World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit limitations in efficiently encoding spatial and temporal structures, particularly in scenarios requiring long-term high-dimensional sequence modelling. To address these issues, we propose a novel recurrent framework, the \textbf{FACT}ored \textbf{S}tate-space (\textbf{FACTS}) model, for spatial-temporal world modelling. The FACTS framework constructs a graph-structured memory with a routing mechanism that learns permutable memory representations, ensuring invariance to input permutations while adapting through selective state-space propagation. Furthermore, FACTS supports parallel computation of high-dimensional sequences. We empirically evaluate FACTS across diverse tasks, including multivariate time series forecasting and object-centric world modelling, demonstrating that it consistently outperforms or matches specialised state-of-the-art models, despite its general-purpose world modelling design.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ec840755867d3f5cf175cb57de963f042297f4ef" target='_blank'>FACTS: A Factored State-Space Framework For World Modelling</a></td>
+          <td>
+            Li Nanbo, Firas Laakom, Yucheng Xu, Wenyi Wang, Jurgen Schmidhuber
+          </td>
+          <td>2024-10-28</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>7</td>
+        </tr>
+    
         <tr id="Time series with long-term structure arise in a variety of contexts and capturing this temporal structure is a critical challenge in time series analysis for both inference and forecasting settings. Traditionally, state space models have been successful in providing uncertainty estimates of trajectories in the latent space. More recently, deep learning, attention-based approaches have achieved state of the art performance for sequence modeling, though often require large amounts of data and parameters to do so. We propose Stanza, a nonlinear, non-stationary state space model as an intermediate approach to fill the gap between traditional models and modern deep learning approaches for complex time series. Stanza strikes a balance between competitive forecasting accuracy and probabilistic, interpretable inference for highly structured time series. In particular, Stanza achieves forecasting accuracy competitive with deep LSTMs on real-world datasets, especially for multi-step ahead forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/9c619e58d073772ff298228d47afcab625c7f37a" target='_blank'>Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series</a></td>
@@ -90,6 +102,18 @@ hide:
           <td>50</td>
         </tr>
     
+        <tr id="Forecasting the behaviour of complex dynamical systems such as interconnected sensor networks characterized by high-dimensional multivariate time series(MTS) is of paramount importance for making informed decisions and planning for the future in a broad spectrum of applications. Graph forecasting networks(GFNs) are well-suited for forecasting MTS data that exhibit spatio-temporal dependencies. However, most prior works of GFN-based methods on MTS forecasting rely on domain-expertise to model the nonlinear dynamics of the system, but neglect the potential to leverage the inherent relational-structural dependencies among time series variables underlying MTS data. On the other hand, contemporary works attempt to infer the relational structure of the complex dependencies between the variables and simultaneously learn the nonlinear dynamics of the interconnected system but neglect the possibility of incorporating domain-specific prior knowledge to improve forecast accuracy. To this end, we propose a hybrid architecture that combines explicit prior knowledge with implicit knowledge of the relational structure within the MTS data. It jointly learns intra-series temporal dependencies and inter-series spatial dependencies by encoding time-conditioned structural spatio-temporal inductive biases to provide more accurate and reliable forecasts. It also models the time-varying uncertainty of the multi-horizon forecasts to support decision-making by providing estimates of prediction uncertainty. The proposed architecture has shown promising results on multiple benchmark datasets and outperforms state-of-the-art forecasting methods by a significant margin. We report and discuss the ablation studies to validate our forecasting architecture.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/05cd3afc8208f1c0dd61b09a90f35dd42497e175" target='_blank'>Multi-Knowledge Fusion Network for Time Series Representation Learning</a></td>
+          <td>
+            Sakhinana Sagar Srinivas, Shivam Gupta, Krishna Sai Sudhir Aripirala, Venkataramana Runkana
+          </td>
+          <td>2024-08-22</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>3</td>
+        </tr>
+    
         <tr id="Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time series datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/7a1e5377b08489c2969f73c56efc557e34f578e1" target='_blank'>Relational State-Space Model for Stochastic Multi-Object Systems</a></td>
@@ -114,34 +138,6 @@ hide:
           <td>12</td>
         </tr>
     
-        <tr id="Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep learning layers for efficient sequence modeling. However, we find fundamental limitations with these prior approaches, proving their SSM representations cannot express autoregressive time series processes. We thus introduce SpaceTime, a new state-space time series architecture that improves all three criteria. For expressivity, we propose a new SSM parameterization based on the companion matrix -- a canonical representation for discrete-time processes -- which enables SpaceTime's SSM layers to learn desirable autoregressive processes. For long horizon forecasting, we introduce a"closed-loop"variation of the companion SSM, which enables SpaceTime to predict many future time-steps by generating its own layer-wise inputs. For efficient training and inference, we introduce an algorithm that reduces the memory and compute of a forward pass with the companion matrix. With sequence length $\ell$ and state-space size $d$, we go from $\tilde{O}(d \ell)$ na\"ively to $\tilde{O}(d + \ell)$. In experiments, our contributions lead to state-of-the-art results on extensive and diverse benchmarks, with best or second-best AUROC on 6 / 7 ECG and speech time series classification, and best MSE on 14 / 16 Informer forecasting tasks. Furthermore, we find SpaceTime (1) fits AR($p$) processes that prior deep SSMs fail on, (2) forecasts notably more accurately on longer horizons than prior state-of-the-art, and (3) speeds up training on real-world ETTh1 data by 73% and 80% relative wall-clock time over Transformers and LSTMs.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a7d68b1702af08ce4dbbf2cd0b083e744ae5c6be" target='_blank'>Effectively Modeling Time Series with Simple Discrete State Spaces</a></td>
-          <td>
-            Michael Zhang, Khaled Kamal Saab, Michael Poli, Tri Dao, Karan Goel, Christopher Ré
-          </td>
-          <td>2023-03-16</td>
-          <td>ArXiv</td>
-          <td>31</td>
-          <td>45</td>
-        </tr>
-    
-        <tr id="
- 
- Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks. Our learning algorithm simultaneously learns a compiled inference network and the generative model, leveraging a structured variational approximation parameterized by recurrent neural networks to mimic the posterior distribution. We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood.
- 
-">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2af17f153e3fd71e15db9216b972aef222f46617" target='_blank'>Structured Inference Networks for Nonlinear State Space Models</a></td>
-          <td>
-            R. G. Krishnan, Uri Shalit, D. Sontag
-          </td>
-          <td>2016-09-30</td>
-          <td>DBLP, MAG, ArXiv</td>
-          <td>435</td>
-          <td>48</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md b/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md
index c1fe89fd..cf399b69 100644
--- a/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md
+++ b/docs/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:10 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:47 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -122,10 +122,22 @@ hide:
           </td>
           <td>2020-07-02</td>
           <td>ArXiv</td>
-          <td>134</td>
+          <td>136</td>
           <td>23</td>
         </tr>
     
+        <tr id="None">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/de3567866929da3a25754f93f917101717008833" target='_blank'>Nonlinear system identification with regularized Tensor Network B-splines</a></td>
+          <td>
+            Ridvan Karagoz, Kim Batselier
+          </td>
+          <td>2020-03-17</td>
+          <td>Autom.</td>
+          <td>18</td>
+          <td>4</td>
+        </tr>
+    
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/a9a3238808d20c5236a7c5f912c20e6dd729f88c" target='_blank'>Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation</a></td>
@@ -150,18 +162,6 @@ hide:
           <td>33</td>
         </tr>
     
-        <tr id="This article discusses a newly developed online manifold learning method, subspace iteration using reduced models (SIRM), for the dimensionality reduction of dynamical systems. This method may be viewed as subspace iteration combined with a model reduction procedure. Specifically, starting with a test solution, the method solves a reduced model to obtain a more precise solution, and it repeats this process until sufficient accuracy is achieved. The reduced model is obtained by projecting the full model onto a subspace that is spanned by the dominant modes of an extended data ensemble. The extended data ensemble in this article contains not only the state vectors of some snapshots of the approximate solution from the previous iteration but also the associated tangent vectors. Therefore, the proposed manifold learning method takes advantage of the information of the original dynamical system to reduce the dynamics. Moreover, the learning procedure is computed in the online stage, as opposed to being compute...">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/418ebf87dea83c922735e9083fe533efc8a04b7f" target='_blank'>An Online Manifold Learning Approach for Model Reduction of Dynamical Systems</a></td>
-          <td>
-            Liqian Peng, K. Mohseni
-          </td>
-          <td>2012-10-10</td>
-          <td>SIAM J. Numer. Anal.</td>
-          <td>22</td>
-          <td>40</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md b/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
index ad599af6..c1aa11ce 100644
--- a/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
+++ b/docs/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:14 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:50 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,8 +50,8 @@ hide:
           </td>
           <td>2019-06-25</td>
           <td>IEEE Access</td>
-          <td>104</td>
-          <td>66</td>
+          <td>103</td>
+          <td>67</td>
         </tr>
     
         <tr id="Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven "new physics" discovery. Specifically, given a trajectory governed by unknown forces, our neural new-physics detector (NNPhD) aims to detect new physics by decomposing the force field into conservative and nonconservative components, which are represented by a Lagrangian neural network (LNN) and an unconstrained neural network, respectively, trained to minimize the force recovery error plus a constant λ times the magnitude of the predicted nonconservative force. We show that a phase transition occurs at λ=1, universally for arbitrary forces. We demonstrate that NNPhD successfully discovers new physics in toy numerical experiments, rediscovering friction (1493) from a damped double pendulum, Neptune from Uranus' orbit (1846), and gravitational waves (2017) from an inspiraling orbit. We also show how NNPhD coupled with an integrator outperforms both an LNN and an unconstrained neural network for predicting the future of a damped double pendulum.">
@@ -109,7 +109,7 @@ hide:
             Philipp Holl, V. Koltun, Nils Thuerey
           </td>
           <td>2021-09-30</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>5</td>
           <td>105</td>
         </tr>
@@ -134,7 +134,7 @@ hide:
           </td>
           <td>2021-05-06</td>
           <td>Physical review letters</td>
-          <td>92</td>
+          <td>93</td>
           <td>82</td>
         </tr>
     
diff --git a/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md b/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
index 67568ea4..10eed999 100644
--- a/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
+++ b/docs/recommendations/3c9961153493370500020c81527b3548c96f81e0.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:02 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:42 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,8 +50,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
     
         <tr id="None">
@@ -62,7 +62,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
     
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2022-11-19</td>
           <td>Proceedings of the Royal Society A</td>
-          <td>12</td>
+          <td>13</td>
           <td>32</td>
         </tr>
     
@@ -135,7 +135,7 @@ hide:
           <td>2021-07-22</td>
           <td>Communications Physics</td>
           <td>20</td>
-          <td>94</td>
+          <td>95</td>
         </tr>
     
         <tr id="Estimating governing equations from observed time-series data is crucial for understanding dynamical systems. From the perspective of system comprehension, the demand for accurate estimation and interpretable results has been particularly emphasized. Herein, we propose a novel data-driven method for estimating the governing equations of dynamical systems based on machine learning with high accuracy and interpretability. The proposed method enhances the estimation accuracy for dynamical systems using sparse modeling by incorporating physical constraints derived from Hamiltonian mechanics. Unlike conventional approaches used for estimating governing equations for dynamical systems, we employ a sparse representation of Hamiltonian, allowing for the estimation. Using noisy observational data, the proposed method demonstrates a capability to achieve accurate parameter estimation and extraction of essential nonlinear terms. In addition, it is shown that estimations based on energy conservation principles exhibit superior accuracy in long-term predictions. These results collectively indicate that the proposed method accurately estimates dynamical systems while maintaining interpretability.">
diff --git a/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md b/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md
index 37498f4c..c8ce8480 100644
--- a/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md
+++ b/docs/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:03 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:42 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -42,6 +42,18 @@ hide:
   </thead>
   <tbody>
     
+        <tr id="This article introduces an advanced Koopman mode decomposition (KMD) technique—coined Featurized Koopman Mode Decomposition (FKMD)—that uses delay embedding and a learned Mahalanobis distance to enhance analysis and prediction of high-dimensional dynamical systems. The delay embedding expands the observation space to better capture underlying manifold structures, while the Mahalanobis distance adjusts observations based on the system’s dynamics. This aids in featurizing KMD in cases where good features are not a priori known. We show that FKMD improves predictions for a high-dimensional linear oscillator, a high-dimensional Lorenz attractor that is partially observed, and a cell signaling problem from cancer research.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/78f159b92f2f6689b738970dc1ce3a8641b2a534" target='_blank'>Featurizing Koopman mode decomposition for robust forecasting</a></td>
+          <td>
+            D. Aristoff, J. Copperman, Nathan Mankovich, Alexander Davies
+          </td>
+          <td>2023-12-14</td>
+          <td>The Journal of Chemical Physics</td>
+          <td>0</td>
+          <td>12</td>
+        </tr>
+    
         <tr id="A common problem in time-series analysis is to predict dynamics with only scalar or partial observations of the underlying dynamical system. For data on a smooth compact manifold, Takens' theorem proves a time-delayed embedding of the partial state is diffeomorphic to the attractor, although for chaotic and highly nonlinear systems, learning these delay coordinate mappings is challenging. We utilize deep artificial neural networks (ANNs) to learn discrete time maps and continuous time flows of the partial state. Given training data for the full state, we also learn a reconstruction map. Thus, predictions of a time series can be made from the current state and several previous observations with embedding parameters determined from time-series analysis. The state space for time evolution is of comparable dimension to reduced order manifold models. These are advantages over recurrent neural network models, which require a high-dimensional internal state or additional memory terms and hyperparameters. We demonstrate the capacity of deep ANNs to predict chaotic behavior from a scalar observation on a manifold of dimension three via the Lorenz system. We also consider multivariate observations on the Kuramoto-Sivashinsky equation, where the observation dimension required for accurately reproducing dynamics increases with the manifold dimension via the spatial extent of the system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/e001a698302d507abbd3a818e0b0aaf84f02e418" target='_blank'>Deep learning delay coordinate dynamics for chaotic attractors from partial observable data</a></td>
@@ -62,8 +74,8 @@ hide:
           </td>
           <td>2022-01-13</td>
           <td>Proceedings of the Royal Society A</td>
-          <td>73</td>
-          <td>66</td>
+          <td>75</td>
+          <td>67</td>
         </tr>
     
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -75,7 +87,7 @@ hide:
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>82</td>
+          <td>83</td>
         </tr>
     
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -87,7 +99,7 @@ hide:
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>82</td>
+          <td>83</td>
         </tr>
     
         <tr id="Many complex phenomena, from weather systems to heartbeat rhythm patterns, are effectively modeled as low-dimensional dynamical systems. Such systems may behave chaotically under certain conditions, and so the ability to detect chaos based on empirical measurement is an important step in characterizing and predicting these processes. Classifying a system as chaotic usually requires estimating its largest Lyapunov exponent, which quantifies the average rate of convergence or divergence of initially close trajectories in state space, and for which a positive value is generally accepted as an operational definition of chaos. Estimating the largest Lyapunov exponent from observations of a process is especially challenging in systems affected by dynamical noise, which is the case for many models of real-world processes, in particular models of biological systems. We describe a novel method for estimating the largest Lyapunov exponent from data, based on training Deep Learning models on synthetically generated trajectories, and demonstrate that this method yields accurate and noise-robust predictions given relatively short inputs and across a range of different dynamical systems. Our method is unique in that it can analyze tree-shaped data, a ubiquitous topology in biological settings, and specifically in dynamics over lineages of cells or organisms. We also characterize the types of input information extracted by our models for their predictions, allowing for a deeper understanding into the different ways by which chaos can be analyzed in different topologies.">
@@ -110,7 +122,7 @@ hide:
           </td>
           <td>2021-06-01</td>
           <td>Science Advances</td>
-          <td>49</td>
+          <td>50</td>
           <td>50</td>
         </tr>
     
@@ -126,30 +138,6 @@ hide:
           <td>50</td>
         </tr>
     
-        <tr id="Complex systems are commonly modeled using nonlinear dynamical systems. These models are often high-dimensional and chaotic. An important goal in studying physical systems through the lens of mathematical models is to determine when the system undergoes changes in qualitative behavior. A detailed description of the dynamics can be difficult or impossible to obtain for high-dimensional and chaotic systems. Therefore, a more sensible goal is to recognize and mark transitions of a system between qualitatively different regimes of behavior. In practice, one is interested in developing techniques for detection of such transitions from sparse observations, possibly contaminated by noise. In this paper we develop a framework to accurately tag different regimes of complex systems based on topological features. In particular, our framework works with a high degree of success in picking out a cyclically orbiting regime from a stationary equilibrium regime in high-dimensional stochastic dynamical systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4201cd896bbab7c98602be04bb744b6c470b0dfb" target='_blank'>Automatic recognition and tagging of topologically different regimes in dynamical systems</a></td>
-          <td>
-            Jesse Berwald, M. Gidea, Mikael Vejdemo-Johansson
-          </td>
-          <td>2013-12-09</td>
-          <td>ArXiv</td>
-          <td>35</td>
-          <td>22</td>
-        </tr>
-    
-        <tr id="None">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0515c6b8af04ab03d06dcbb61caf851ff899fc37" target='_blank'>Dynamics reconstruction and classification via Koopman features</a></td>
-          <td>
-            Wei Zhang, Yao-Chi Yu, Jr-Shin Li
-          </td>
-          <td>2019-06-24</td>
-          <td>Data Mining and Knowledge Discovery</td>
-          <td>3</td>
-          <td>28</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md b/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md
index 76ec4120..3286942f 100644
--- a/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md
+++ b/docs/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:34 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:08 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2023-03-01</td>
           <td>ArXiv</td>
-          <td>16</td>
+          <td>17</td>
           <td>65</td>
         </tr>
     
@@ -98,7 +98,7 @@ hide:
           </td>
           <td>2023-03-01</td>
           <td>ArXiv</td>
-          <td>16</td>
+          <td>17</td>
           <td>65</td>
         </tr>
     
@@ -133,9 +133,9 @@ hide:
             Steven Cheng-Xian Li, Benjamin M Marlin
           </td>
           <td>2020-07-12</td>
-          <td>DBLP, MAG, ArXiv</td>
-          <td>53</td>
-          <td>33</td>
+          <td>ArXiv, MAG, DBLP</td>
+          <td>54</td>
+          <td>34</td>
         </tr>
     
         <tr id="We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple wearable sensors. We focus on two under-explored practical challenges arising in such settings: (i) Each task may have a different subset of sensors, i.e., providing different partial observations of the underlying ‘system’. This restriction can be due to different manufacturers in the former case, and people wearing more or less measurement devices in the latter (ii) We are not allowed to store or re-access data from a task once it has been observed at the task level. This may be due to privacy considerations in the case of people, or legal restrictions placed by machine owners. Nevertheless, we would like to (a) improve performance on subsequent tasks using experience from completed tasks as well as (b) continue to perform better on past tasks, e.g., update the model and improve predictions on even the first machine after learning from subsequently observed ones. We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In this work, we address this shortcoming of existing methods. To this end, we learn task-specific generative models and classifiers, and use these to augment data for target tasks. Since the input dimensions across tasks vary, we propose a novel conditioning module based on graph neural networks to aid a standard recurrent neural network. We evaluate the efficacy of the proposed approach on three publicly available datasets corresponding to two activity recognition tasks (classification) and one prognostics task (regression). We demonstrate that it is possible to significantly enhance the performance on future and previous tasks while learning continuously from VID tasks without storing data.">
@@ -147,7 +147,7 @@ hide:
           <td>2021-12-01</td>
           <td>2021 IEEE International Conference on Data Mining (ICDM)</td>
           <td>17</td>
-          <td>27</td>
+          <td>28</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md b/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md
index 9eb12595..5b5d1197 100644
--- a/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md
+++ b/docs/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:08 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:46 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,8 +74,8 @@ hide:
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>116</td>
-          <td>66</td>
+          <td>119</td>
+          <td>67</td>
         </tr>
     
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
diff --git a/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md b/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md
index cec0ed16..7002d394 100644
--- a/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md
+++ b/docs/recommendations/4a111f7a3b56d0468f13104999844885157ef17d.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:40 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:17 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -74,19 +74,19 @@ hide:
           </td>
           <td>2024-09-24</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>9</td>
         </tr>
     
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
           <td>
             Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>69</td>
           <td>18</td>
         </tr>
     
@@ -97,8 +97,8 @@ hide:
             Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long
           </td>
           <td>2024-02-04</td>
-          <td>DBLP, ArXiv</td>
-          <td>18</td>
+          <td>ArXiv, DBLP</td>
+          <td>21</td>
           <td>67</td>
         </tr>
     
@@ -110,10 +110,22 @@ hide:
           </td>
           <td>2022-11-27</td>
           <td>ArXiv</td>
-          <td>701</td>
+          <td>724</td>
           <td>35</td>
         </tr>
     
+        <tr id="Developing a foundation model for time series forecasting across diverse domains has attracted significant attention in recent years. Existing works typically assume regularly sampled, well-structured data, limiting their applicability to more generalized scenarios where time series often contain missing values, unequal sequence lengths, and irregular time intervals between measurements. To cover diverse domains and handle variable regularities, we propose FlexTSF, a universal time series forecasting model that possesses better generalization and natively support both regular and irregular time series. FlexTSF produces forecasts in an autoregressive manner and incorporates three novel designs: VT-Norm, a normalization strategy to ablate data domain barriers, IVP Patcher, a patching module to learn representations from flexibly structured time series, and LED attention, an attention mechanism to seamlessly integrate these two and propagate forecasts with awareness of domain and time information. Experiments on 12 datasets show that FlexTSF outperforms state-of-the-art forecasting models respectively designed for regular and irregular time series. Furthermore, after self-supervised pre-training, FlexTSF shows exceptional performance in both zero-shot and few-show settings for time series forecasting.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/45521591fd8c46068f0a8712b43ce6cdaac1edea" target='_blank'>FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities</a></td>
+          <td>
+            Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl, Simon Gottschalk
+          </td>
+          <td>2024-10-30</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>6</td>
+        </tr>
+    
         <tr id="Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained significant interest. However, current methods often overlook the multi-scale nature of time series, which is essential for accurate forecasting. To address this, we propose HiMTM, a hierarchical multi-scale masked time series modeling with self-distillation for long-term forecasting. HiMTM integrates four key components: (1) hierarchical multi-scale transformer (HMT) to capture temporal information at different scales; (2) decoupled encoder-decoder (DED) that directs the encoder towards feature extraction while the decoder focuses on pretext tasks; (3) hierarchical self-distillation (HSD) for multi-stage feature-level supervision signals during pre-training; and (4) cross-scale attention fine-tuning (CSA-FT) to capture dependencies between different scales for downstream tasks. These components collectively enhance multi-scale feature extraction in masked time series modeling, improving forecasting accuracy. Extensive experiments on seven mainstream datasets show that HiMTM surpasses state-of-the-art self-supervised and end-to-end learning methods by a considerable margin of 3.16-68.54\%. Additionally, HiMTM outperforms the latest robust self-supervised learning method, PatchTST, in cross-domain forecasting by a significant margin of 2.3\%. The effectiveness of HiMTM is further demonstrated through its application in natural gas demand forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/d945eae22457648e191742c37bcad88a114ce703" target='_blank'>HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting</a></td>
@@ -126,18 +138,6 @@ hide:
           <td>7</td>
         </tr>
     
-        <tr id="The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks, first in natural language processing and later in computer vision. This model is based on the attention mechanism and is able to capture complex semantic relationships between a variety of patterns present in the input data. Precisely because of these characteristics, the Transformer has recently been exploited for time series forecasting problems, assuming a natural adaptability to the domain of continuous numerical series. Despite the acclaimed results in the literature, some works have raised doubts about the robustness and effectiveness of this approach. In this paper, we further investigate the effectiveness of Transformer-based models applied to the domain of time series forecasting, demonstrate their limitations, and propose a set of alternative models that are better performing and significantly less complex. In particular, we empirically show how simplifying Transformer-based forecasting models almost always leads to an improvement, reaching state of the art performance. We also propose shallow models without the attention mechanism, which compete with the overall state of the art in long time series forecasting, and demonstrate their ability to accurately predict time series over extremely long windows. From a methodological perspective, we show how it is always necessary to use a simple baseline to verify the effectiveness of proposed models, and finally, we conclude the paper with a reflection on recent research paths and the opportunity to follow trends and hypes even where it may not be necessary.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b98bc7d48357932537fc9ba6035e3b6bd1018ae6" target='_blank'>Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning</a></td>
-          <td>
-            Riccardo Ughi, Eugenio Lomurno, Matteo Matteucci
-          </td>
-          <td>2023-04-10</td>
-          <td>DBLP, ArXiv</td>
-          <td>1</td>
-          <td>6</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md b/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md
index 3a5f89da..a8c3d1db 100644
--- a/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md
+++ b/docs/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:43 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:23 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -121,7 +121,7 @@ hide:
             Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Bernie Wang, Xifeng Yan
           </td>
           <td>2021-02-13</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>63</td>
           <td>65</td>
         </tr>
diff --git a/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md b/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md
index 4ec85daf..582cde05 100644
--- a/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md
+++ b/docs/recommendations/5d150cec2775f9bc863760448f14104cc8f42368.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:00 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:41 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,8 +50,8 @@ hide:
           </td>
           <td>2019-03-29</td>
           <td>Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>645</td>
-          <td>66</td>
+          <td>648</td>
+          <td>67</td>
         </tr>
     
         <tr id="None">
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
     
@@ -147,7 +147,7 @@ hide:
           <td>2014-12-10</td>
           <td>The European Physical Journal Special Topics</td>
           <td>64</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
     
         <tr id="None">
@@ -159,7 +159,7 @@ hide:
           <td>2014-12-01</td>
           <td>The European Physical Journal Special Topics</td>
           <td>7</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md b/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md
index 74c747db..2c0aec77 100644
--- a/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md
+++ b/docs/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:01 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:41 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2022-07-01</td>
           <td>Nature Computational Science</td>
-          <td>76</td>
+          <td>77</td>
           <td>21</td>
         </tr>
     
@@ -63,7 +63,7 @@ hide:
           <td>2024-05-07</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
     
         <tr id="None">
@@ -87,7 +87,7 @@ hide:
           <td>2021-07-22</td>
           <td>Communications Physics</td>
           <td>20</td>
-          <td>94</td>
+          <td>95</td>
         </tr>
     
         <tr id="The extraction of models from data (in a sense, the “understanding” of the physical laws giving rise to the data) is a fundamental cognitive as well as scientific challenge. We show a geometric/analytic learning algorithm capable of creating minimal descriptions of parametrically dependent unknown nonlinear dynamical systems. This is accomplished by the data-driven discovery of useful intrinsic-state variables and parameters in terms of which one can empirically model the underlying dynamics. We discuss an informed observation geometry that enables us to formulate models without first principles as well as without closed form equations. (See pp. E7865–E7874.)">
@@ -114,18 +114,6 @@ hide:
           <td>73</td>
         </tr>
     
-        <tr id="High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ecf61b26615fc12f9edc4826dd81a23ac0d72f37" target='_blank'>Physics-informed deep-learning applications to experimental fluid mechanics</a></td>
-          <td>
-            Hamidreza Eivazi, Yuning Wang, R. Vinuesa
-          </td>
-          <td>2022-03-29</td>
-          <td>Measurement Science and Technology</td>
-          <td>25</td>
-          <td>39</td>
-        </tr>
-    
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/606824bebf4e444fca9b988823883f1ec99f19ec" target='_blank'>A physics-informed operator regression framework for extracting data-driven continuum models</a></td>
@@ -150,6 +138,19 @@ hide:
           <td>47</td>
         </tr>
     
+        <tr id="
+ In the context of science, the well-known adage “a picture is worth a thousand words” might well be “a model is worth a thousand datasets.” Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reconciling data that is at odds with simplified models without requiring "big data". In this work demonstrate how a mathematical object, which we denote universal differential equations (UDEs), can be utilized as a theoretical underpinning to a diverse array of problems in scientific machine learning to yield efficient algorithms and generalized approaches. The UDE model augments scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating the training of physics-informed neural networks and large-eddy simulations, can all be transformed into UDE training problems that are efficiently solved by a single software methodology.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/696b388ee6221c6dbcfd647a06883b2bfee773d9" target='_blank'>Universal Differential Equations for Scientific Machine Learning</a></td>
+          <td>
+            Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, K. Zubov, R. Supekar, Dominic J. Skinner, A. Ramadhan
+          </td>
+          <td>2020-01-13</td>
+          <td>ArXiv</td>
+          <td>509</td>
+          <td>26</td>
+        </tr>
+    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md b/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md
index b8d72cf0..87487594 100644
--- a/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md
+++ b/docs/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:42 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:21 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2023-04-23</td>
           <td>Sensors (Basel, Switzerland)</td>
-          <td>27</td>
+          <td>28</td>
           <td>77</td>
         </tr>
     
@@ -157,7 +157,7 @@ hide:
             Konstantinos Kallidromitis, Denis A. Gudovskiy, Kozuka Kazuki, Ohama Iku, Luca Rigazio
           </td>
           <td>2021-10-24</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>10</td>
           <td>13</td>
         </tr>
diff --git a/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md b/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md
index c68e1421..8dcb7561 100644
--- a/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md
+++ b/docs/recommendations/73dd9c49f205280991826b2ea4b50344203916b4.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:11 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:49 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -110,8 +110,8 @@ hide:
           </td>
           <td>2017-11-15</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>455</td>
-          <td>66</td>
+          <td>461</td>
+          <td>67</td>
         </tr>
     
         <tr id="None">
@@ -134,8 +134,8 @@ hide:
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>220</td>
-          <td>66</td>
+          <td>221</td>
+          <td>67</td>
         </tr>
     
         <tr id="We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squares approach, and provide finite sample error bounds of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data. We show that the auxiliary data can help to reduce the intrinsic system identification error due to noise, at the price of adding a portion of error that is due to the differences between the two system models. We further provide a data-dependent bound that is computable when some prior knowledge about the systems, such as upper bounds on noise levels and model difference, is available. This bound can also be used to determine the weight that should be assigned to the auxiliary data during the model training stage.">
@@ -147,7 +147,7 @@ hide:
           <td>2023-02-08</td>
           <td>ArXiv</td>
           <td>7</td>
-          <td>36</td>
+          <td>37</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md b/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md
index 495cdea7..4c26a2c8 100644
--- a/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md
+++ b/docs/recommendations/883547fdbd88552328a6615ec620f96e39c57018.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:19 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:51 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2021-07-05</td>
           <td>Royal Society Open Science</td>
-          <td>59</td>
+          <td>61</td>
           <td>32</td>
         </tr>
     
@@ -63,7 +63,7 @@ hide:
           <td>2023-01-30</td>
           <td>ArXiv</td>
           <td>11</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
     
         <tr id="None">
@@ -86,8 +86,8 @@ hide:
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>220</td>
-          <td>66</td>
+          <td>221</td>
+          <td>67</td>
         </tr>
     
         <tr id="We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data. We recast the SINDy method within a Bayesian framework and use Gaussian approximations for the prior and likelihood to speed up computation. The resulting method, Bayesian-SINDy, not only quantifies uncertainty in the parameters estimated but also is more robust when learning the correct model from limited and noisy data. Using both synthetic and real-life examples such as Lynx-Hare population dynamics, we demonstrate the effectiveness of the new framework in learning correct model equations and compare its computational and data efficiency with existing methods. Because Bayesian-SINDy can quickly assimilate data and is robust against noise, it is particularly suitable for biological data and real-time system identification in control. Its probabilistic framework also enables the calculation of information entropy, laying the foundation for an active learning strategy.">
@@ -110,8 +110,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
     
         <tr id="The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present.">
diff --git a/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md b/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md
index 4da159d6..65086187 100644
--- a/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md
+++ b/docs/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:04 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:43 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -42,18 +42,6 @@ hide:
   </thead>
   <tbody>
     
-        <tr id="Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints. However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control. In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC. In particular, we focus on the sparse identification of nonlinear dynamics (SINDy) algorithm and show how it may be used with MPC on an infectious disease control example. We compare the performance against MPC based on a linear dynamic mode decomposition (DMD) model. Code is provided to run the tutorial examples and may be modified to extend this data-driven control framework to arbitrary nonlinear systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/700552dde778bd1f6b76f7237e9b561597b859ba" target='_blank'>SINDy with Control: A Tutorial</a></td>
-          <td>
-            Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton
-          </td>
-          <td>2021-08-30</td>
-          <td>2021 60th IEEE Conference on Decision and Control (CDC)</td>
-          <td>51</td>
-          <td>66</td>
-        </tr>
-    
         <tr id="In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues in order to exploit SINDy also in these challenging contexts.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/99877062e70fae24c531d58aa880fa443ec10c9b" target='_blank'>SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study</a></td>
@@ -86,8 +74,8 @@ hide:
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>220</td>
-          <td>66</td>
+          <td>221</td>
+          <td>67</td>
         </tr>
     
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetries and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -122,8 +110,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
     
         <tr id="Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy, we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. The library of candidate terms is extended for the identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling techniques, are also implemented to improve performance for real-world data. Finally, we provide a range of new optimization algorithms, including several sparse regression techniques and algorithms to enforce and promote inequality constraints and stability. Together, these updates enable entirely new SINDy model discovery capabilities that have not been reported in the literature, such as constrained PDE identification and ensembling with different sparse regression optimizers.">
@@ -134,8 +122,8 @@ hide:
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>116</td>
-          <td>66</td>
+          <td>119</td>
+          <td>67</td>
         </tr>
     
         <tr id="Identifying dynamical systems characterized by nonlinear parameters presents significant challenges in deriving mathematical models that enhance understanding of physics. Traditional methods, such as Sparse Identification of Nonlinear Dynamics (SINDy) and symbolic regression, can extract governing equations from observational data; however, they also come with distinct advantages and disadvantages. This paper introduces a novel method within the SINDy framework, termed ADAM-SINDy, which synthesizes the strengths of established approaches by employing the ADAM optimization algorithm. This facilitates the simultaneous optimization of nonlinear parameters and coefficients associated with nonlinear candidate functions, enabling precise parameter estimation without requiring prior knowledge of nonlinear characteristics such as trigonometric frequencies, exponential bandwidths, or polynomial exponents, thereby addressing a key limitation of SINDy. Through an integrated global optimization, ADAM-SINDy dynamically adjusts all unknown variables in response to data, resulting in an adaptive identification procedure that reduces the sensitivity to the library of candidate functions. The performance of the ADAM-SINDy methodology is demonstrated across a spectrum of dynamical systems, including benchmark coupled nonlinear ordinary differential equations such as oscillators, chaotic fluid flows, reaction kinetics, pharmacokinetics, as well as nonlinear partial differential equations (wildfire transport). The results demonstrate significant improvements in identifying parameterized dynamical systems and underscore the importance of concurrently optimizing all parameters, particularly those characterized by nonlinear parameters. These findings highlight the potential of ADAM-SINDy to extend the applicability of the SINDy framework in addressing more complex challenges in dynamical system identification.">
diff --git a/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md b/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md
index 99bec515..052c808a 100644
--- a/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md
+++ b/docs/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:23 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:54 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -51,7 +51,7 @@ hide:
           <td>2018-07-14</td>
           <td>ArXiv</td>
           <td>9</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
     
         <tr id="Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact model of sparse regression with an $\ell_0$ constraint restricting the support of the estimators is a challenging (\NP-hard) non-convex optimization problem. In this paper, we derive new strong convex relaxations for sparse regression. These relaxations are based on the ideal (convex-hull) formulations for rank-one quadratic terms with indicator variables. The new relaxations can be formulated as semidefinite optimization problems in an extended space and are stronger and more general than the state-of-the-art formulations, including the perspective reformulation and formulations with the reverse Huber penalty and the minimax concave penalty functions. Furthermore, the proposed rank-one strengthening can be interpreted as a \textit{non-separable, non-convex, unbiased} sparsity-inducing regularizer, which dynamically adjusts its penalty according to the shape of the error function without inducing bias for the sparse solutions. In our computational experiments with benchmark datasets, the proposed conic formulations are solved within seconds and result in near-optimal solutions (with 0.4\% optimality gap) for non-convex $\ell_0$-problems. Moreover, the resulting estimators also outperform alternative convex approaches from a statistical perspective, achieving high prediction accuracy and good interpretability.">
diff --git a/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md b/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
index cfb612ca..c3afc5e2 100644
--- a/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
+++ b/docs/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:44 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:25 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2022-04-23</td>
           <td>ArXiv</td>
-          <td>6</td>
+          <td>7</td>
           <td>50</td>
         </tr>
     
@@ -61,8 +61,8 @@ hide:
             Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan
           </td>
           <td>2023-07-07</td>
-          <td>IEEE transactions on pattern analysis and machine intelligence</td>
-          <td>80</td>
+          <td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td>
+          <td>82</td>
           <td>50</td>
         </tr>
     
@@ -122,8 +122,8 @@ hide:
           </td>
           <td>2022-02-17</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>66</td>
-          <td>43</td>
+          <td>69</td>
+          <td>44</td>
         </tr>
     
         <tr id="Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets.">
diff --git a/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md b/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
index 12cb1c1e..54452b13 100644
--- a/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
+++ b/docs/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:36 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:11 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -98,8 +98,8 @@ hide:
           </td>
           <td>2020-05-24</td>
           <td>Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</td>
-          <td>1075</td>
-          <td>56</td>
+          <td>1082</td>
+          <td>57</td>
         </tr>
     
         <tr id="Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN's effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting.">
@@ -122,7 +122,7 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>46</td>
+          <td>49</td>
           <td>6</td>
         </tr>
     
diff --git a/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md b/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
index 954503ea..de19de59 100644
--- a/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
+++ b/docs/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:33 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:07 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -54,6 +54,18 @@ hide:
           <td>15</td>
         </tr>
     
+        <tr id="In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture dependencies among variables and across time points. The objective of the presented systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and over 150 journal papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive collection of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in future studies. To the best of our knowledge, this is the first systematic literature review presenting a detailed comparison of the results of current spatio-temporal GNN models in different domains. In addition, in its final part this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/e6544d542d13247f620834689ca2378cb7fb04f8" target='_blank'>A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification</a></td>
+          <td>
+            Flavio Corradini, Marco Gori, Carlo Lucheroni, Marco Piangerelli, Martina Zannotti
+          </td>
+          <td>2024-10-29</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>12</td>
+        </tr>
+    
         <tr id="Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/d9916caa8da69496ecc8e0b1c229c73a60d6f861" target='_blank'>TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting</a></td>
@@ -74,7 +86,7 @@ hide:
           </td>
           <td>2023-10-24</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>9</td>
           <td>50</td>
         </tr>
     
@@ -110,7 +122,7 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>46</td>
+          <td>49</td>
           <td>6</td>
         </tr>
     
@@ -150,18 +162,6 @@ hide:
           <td>1</td>
         </tr>
     
-        <tr id="This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification, based on visibility graph representations. Traditional time series classification methods often struggle with high computational complexity and inadequate capture of spatio-temporal dynamics. By representing time series as visibility graphs, it is possible to encode both spatial and temporal dependencies inherent to time series data, while being computationally efficient. Our architecture is fully modular, enabling flexible experimentation with different models and representations. We employ directed visibility graphs encoded with in-degree and PageRank features to improve the representation of time series, ensuring efficient computation while enhancing the model's ability to capture long-range dependencies in the data. We show the robustness and generalization capability of the proposed architecture across a diverse set of classification tasks and against a traditional model. Our work represents a significant advancement in the application of GNNs for time series analysis, offering a powerful and flexible framework for future research and practical implementations.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1be874b113152ade7584757402c8129e5e036526" target='_blank'>Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification</a></td>
-          <td>
-            Paulo Coelho, Raul Araju, Lu'is Ramos, Samir Saliba, Renato Vimieiro
-          </td>
-          <td>2024-10-12</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md b/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md
index 58bc4c4a..3833a218 100644
--- a/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md
+++ b/docs/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:35 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:10 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,7 +50,7 @@ hide:
           </td>
           <td>2023-10-24</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>9</td>
           <td>50</td>
         </tr>
     
@@ -62,7 +62,7 @@ hide:
           </td>
           <td>2022-09-14</td>
           <td>ArXiv</td>
-          <td>33</td>
+          <td>34</td>
           <td>50</td>
         </tr>
     
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>46</td>
+          <td>49</td>
           <td>6</td>
         </tr>
     
diff --git a/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md b/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md
index 3dd7e513..9dcf24a0 100644
--- a/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md
+++ b/docs/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:42 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:19 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -54,18 +54,6 @@ hide:
           <td>3</td>
         </tr>
     
-        <tr id="We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning diverse domains. In our proposed framework, the task of multivariate time series forecasting is formulated as a channel-wise next curve shape prediction problem, where each time series sample is represented as a sequence of non-overlapping curve shapes with a unified numerical magnitude. GTT is trained to predict the next curve shape based on a window of past curve shapes in a channel-wise manner. Experimental results demonstrate that GTT exhibits superior zero-shot multivariate forecasting capabilities on unseen time series datasets, even surpassing state-of-the-art supervised baselines. Additionally, we investigate the impact of varying GTT model parameters and training dataset scales, observing that the scaling law also holds in the context of zero-shot multivariate time series forecasting.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/15ea0190593662402d0493958a53d5ca3b8e8406" target='_blank'>Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction</a></td>
-          <td>
-            Cheng Feng, Long Huang, Denis Krompass
-          </td>
-          <td>2024-02-12</td>
-          <td>ArXiv</td>
-          <td>4</td>
-          <td>15</td>
-        </tr>
-    
         <tr id="In recent years, generative pre-trained paradigms such as Large Language Models (LLMs) and Large Vision Models (LVMs) have achieved revolutionary advancements and widespread real-world applications. Particularly, the emergence of pre-trained LLMs-based temporal works, compared to previous deep model approaches, has demonstrated superior generalization and robustness, showcasing the potential of generative pre-trained paradigms as foundation models for time series. However, those LLMs-based works mainly focus on cross-modal research, i.e., leveraging the language capabilities of LLMs in time series contexts. Although they have achieved impressive performance, there still exist the issues of concept drift caused by differences in data distribution and inflexibility caused by misalignment of dimensions. To this end, inspired by recent work on LVMs, we reconsider the paradigm of time series modeling. In this paper, we comprehensively explore, for the first time, the effectiveness and superiority of the Generative Pre-trained Diffusion (GPD) paradigm in real-world multivariate time series forecasting (TSF). Specifically, to mitigate performance bias introduced by sophisticated networks, we propose a straightforward MLP diffusion network for unconditional modeling of time series. Then we employ a zero-shot and tuning-free method to predict (generate) future data using historical data as prompts. The GPD paradigm is established on the time series modality, effectively preventing the phenomenon of concept drift, and enabling flexible forecasting of arbitrary lengths. We demonstrate that the GPD paradigm achieves comprehensive performance and generalization comparable to current SOTA LLM-based and deep model paradigms on mainstream benchmarks and various TSF tasks. Extensive experiments validate the potential of the GPD paradigm and its assistance in future related research.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/818793961396bbe4c744932676f0ff9cc8385bed" target='_blank'>Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting</a></td>
@@ -78,16 +66,16 @@ hide:
           <td>5</td>
         </tr>
     
-        <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
+        <tr id="We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning diverse domains. In our proposed framework, the task of multivariate time series forecasting is formulated as a channel-wise next curve shape prediction problem, where each time series sample is represented as a sequence of non-overlapping curve shapes with a unified numerical magnitude. GTT is trained to predict the next curve shape based on a window of past curve shapes in a channel-wise manner. Experimental results demonstrate that GTT exhibits superior zero-shot multivariate forecasting capabilities on unseen time series datasets, even surpassing state-of-the-art supervised baselines. Additionally, we investigate the impact of varying GTT model parameters and training dataset scales, observing that the scaling law also holds in the context of zero-shot multivariate time series forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/15ea0190593662402d0493958a53d5ca3b8e8406" target='_blank'>Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction</a></td>
           <td>
-            Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
+            Cheng Feng, Long Huang, Denis Krompass
           </td>
-          <td>2024-03-12</td>
+          <td>2024-02-12</td>
           <td>ArXiv</td>
-          <td>63</td>
-          <td>18</td>
+          <td>4</td>
+          <td>15</td>
         </tr>
     
         <tr id="Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.">
@@ -98,10 +86,22 @@ hide:
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>23</td>
           <td>40</td>
         </tr>
     
+        <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td>
+            Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
+          </td>
+          <td>2024-03-12</td>
+          <td>ArXiv</td>
+          <td>69</td>
+          <td>18</td>
+        </tr>
+    
         <tr id="Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training and struggle with poor generalization performance on new domains. Foundation models aim to overcome this limitation. Pre-trained on large-scale language or time series data, they exhibit promising inferencing capabilities in new or unseen data. This has spurred a surge in new TSF foundation models. We propose a new benchmark, FoundTS, to enable thorough and fair evaluation and comparison of such models. FoundTS covers a variety of TSF foundation models, including those based on large language models and those pretrained on time series. Next, FoundTS supports different forecasting strategies, including zero-shot, few-shot, and full-shot, thereby facilitating more thorough evaluations. Finally, FoundTS offers a pipeline that standardizes evaluation processes such as dataset splitting, loading, normalization, and few-shot sampling, thereby facilitating fair evaluations. Building on this, we report on an extensive evaluation of TSF foundation models on a broad range of datasets from diverse domains and with different statistical characteristics. Specifically, we identify pros and cons and inherent limitations of existing foundation models, and we identify directions for future model design. We make our code and datasets available at https://anonymous.4open.science/r/FoundTS-C2B0.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/c245a7d561855d7bef6c758e45e772129b52dacb" target='_blank'>FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting</a></td>
@@ -135,7 +135,7 @@ hide:
           <td>2024-02-07</td>
           <td>ArXiv</td>
           <td>8</td>
-          <td>7</td>
+          <td>8</td>
         </tr>
     
   </tbody>
diff --git a/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md b/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md
index d08731e5..72ee5f26 100644
--- a/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md
+++ b/docs/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:20 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:53 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -62,8 +62,8 @@ hide:
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
     
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetries and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -86,8 +86,8 @@ hide:
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>116</td>
-          <td>66</td>
+          <td>119</td>
+          <td>67</td>
         </tr>
     
         <tr id="Sparse regression has emerged as a popular technique for learning dynamical systems from temporal data, beginning with the SINDy (Sparse Identification of Nonlinear Dynamics) framework proposed by arXiv:1509.03580. Quantifying the uncertainty inherent in differential equations learned from data remains an open problem, thus we propose leveraging recent advances in statistical inference for sparse regression to address this issue. Focusing on systems of ordinary differential equations (ODEs), SINDy assumes that each equation is a parsimonious linear combination of a few candidate functions, such as polynomials, and uses methods such as sequentially-thresholded least squares or the Lasso to identify a small subset of these functions that govern the system's dynamics. We instead employ bias-corrected versions of the Lasso and ridge regression estimators, as well as an empirical Bayes variable selection technique known as SEMMS, to estimate each ODE as a linear combination of terms that are statistically significant. We demonstrate through simulations that this approach allows us to recover the functional terms that correctly describe the dynamics more often than existing methods that do not account for uncertainty.">
@@ -110,7 +110,7 @@ hide:
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
     
@@ -123,7 +123,7 @@ hide:
           <td>2020-04-17</td>
           <td>arXiv: Dynamical Systems</td>
           <td>51</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
     
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
@@ -146,22 +146,10 @@ hide:
           </td>
           <td>2017-12-06</td>
           <td>The Journal of chemical physics</td>
-          <td>199</td>
+          <td>200</td>
           <td>46</td>
         </tr>
     
-        <tr id="Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields. However, it remains an open question to know when model discovery algorithms based on sparse regression can actually recover the underlying physical processes. In this work, we show the design matrices used to infer the equations by sparse regression can violate the irrepresentability condition (IRC) of the Lasso, even when derived from analytical PDE solutions (i.e. without additional noise). Sparse regression techniques which can recover the true underlying model under violated IRC conditions are therefore required, leading to the introduction of the randomised adaptive Lasso. We show once the latter is integrated within the deep learning model discovery framework DeepMod, a wide variety of nonlinear and chaotic canonical PDEs can be recovered: (1) up to $\mathcal{O}(2)$ higher noise-to-sample ratios than state-of-the-art algorithms, (2) with a single set of hyperparameters, which paves the road towards truly automated model discovery.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/45633bde87d3fecdb0ef577036f2465e70c51f24" target='_blank'>Sparsistent Model Discovery</a></td>
-          <td>
-            Georges Tod, G. Both, R. Kusters
-          </td>
-          <td>2021-06-22</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>13</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md b/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md
index e4981fd2..0fe04551 100644
--- a/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md
+++ b/docs/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:39 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:15 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -50,10 +50,22 @@ hide:
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>23</td>
           <td>40</td>
         </tr>
     
+        <tr id="Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-series into the future. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks compared to supervised deep learning methods, statistical models, as well as other time-series foundation models. Interestingly, our in-context fine-tuning approach even rivals the performance of a foundation model that is explicitly fine-tuned on the target domain.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/1216a29c0beb36a2b621b59d36877edf05afcf8c" target='_blank'>In-Context Fine-Tuning for Time-Series Foundation Models</a></td>
+          <td>
+            Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou
+          </td>
+          <td>2024-10-31</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>14</td>
+        </tr>
+    
         <tr id="Large language models (LLMs) are being applied to time series forecasting. But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance -- in most cases, the results even improve! We also find that despite their significant computational cost, pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings. Additionally, we explore time series encoders and find that patching and attention structures perform similarly to LLM-based forecasters.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/df0d604b8e8e3b2947d9865d735f204c08635012" target='_blank'>Are Language Models Actually Useful for Time Series Forecasting?</a></td>
@@ -62,7 +74,7 @@ hide:
           </td>
           <td>2024-06-22</td>
           <td>ArXiv</td>
-          <td>11</td>
+          <td>13</td>
           <td>3</td>
         </tr>
     
@@ -86,7 +98,7 @@ hide:
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>184</td>
+          <td>195</td>
           <td>9</td>
         </tr>
     
@@ -122,7 +134,7 @@ hide:
           </td>
           <td>2023-08-16</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>22</td>
           <td>3</td>
         </tr>
     
@@ -134,22 +146,10 @@ hide:
           </td>
           <td>2024-09-24</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>9</td>
         </tr>
     
-        <tr id="Time series data are ubiquitous across various domains, making time series analysis critically important. Traditional time series models are task-specific, featuring singular functionality and limited generalization capacity. Recently, large language foundation models have unveiled their remarkable capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability. This success has sparked interest in the exploration of foundation models to solve multiple time series challenges simultaneously. There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series. They both contribute to the development of a unified model that is highly generalizable, versatile, and comprehensible for time series analysis. This survey offers a 3E analytical framework for comprehensive examination of related research. Specifically, we examine existing works from three dimensions, namely Effectiveness, Efficiency and Explainability. In each dimension, we focus on discussing how related works devise tailored solution by considering unique challenges in the realm of time series. Furthermore, we provide a domain taxonomy to help followers keep up with the domain-specific advancements. In addition, we introduce extensive resources to facilitate the field's development, including datasets, open-source, time series libraries. A GitHub repository is also maintained for resource updates (https://github.com/start2020/Awesome-TimeSeries-LLM-FM).">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/522828823ff726598675971036fff01b57320578" target='_blank'>A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model</a></td>
-          <td>
-            Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, F. Tsung
-          </td>
-          <td>2024-05-03</td>
-          <td>ArXiv</td>
-          <td>7</td>
-          <td>47</td>
-        </tr>
-    
   </tbody>
   <tfoot>
     <tr>
diff --git a/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md b/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md
index 48a2c989..6d974095 100644
--- a/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md
+++ b/docs/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90.md
@@ -11,7 +11,7 @@ hide:
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:48 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:30 UTC</i>
   </p>
   
   <div class="note info" onclick="startIntro()">
@@ -49,8 +49,8 @@ hide:
             Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long
           </td>
           <td>2024-02-04</td>
-          <td>DBLP, ArXiv</td>
-          <td>18</td>
+          <td>ArXiv, DBLP</td>
+          <td>21</td>
           <td>67</td>
         </tr>
     
@@ -86,7 +86,7 @@ hide:
           </td>
           <td>2024-02-26</td>
           <td>ArXiv</td>
-          <td>6</td>
+          <td>7</td>
           <td>4</td>
         </tr>
     
@@ -116,13 +116,13 @@ hide:
     
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
           <td>
             Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>69</td>
           <td>18</td>
         </tr>
     
@@ -134,7 +134,7 @@ hide:
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>71</td>
           <td>25</td>
         </tr>
     
diff --git a/site/Symbolic regression/index.html b/site/Symbolic regression/index.html
index 2977b9df..bad5505f 100644
--- a/site/Symbolic regression/index.html	
+++ b/site/Symbolic regression/index.html	
@@ -451,7 +451,7 @@ <h1>Symbolic regression</h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:38 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:06:01 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -515,9 +515,9 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
             S. Brunton, J. Proctor, J. Kutz
           </td>
           <td>2015-09-11</td>
-          <td>Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>Proceedings of the National Academy of Sciences of the United States of America, Proceedings of the National Academy of Sciences</td>
+          <td>3408</td>
+          <td>67</td>
 
             <td><a href='../recommendations/5d150cec2775f9bc863760448f14104cc8f42368' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -556,8 +556,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2019-03-29</td>
           <td>Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>645</td>
-          <td>66</td>
+          <td>648</td>
+          <td>67</td>
 
             <td><a href='../recommendations/3c9961153493370500020c81527b3548c96f81e0' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -576,8 +576,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2016-08-18</td>
           <td>Nature Communications</td>
-          <td>462</td>
-          <td>66</td>
+          <td>468</td>
+          <td>67</td>
 
             <td><a href='../recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -596,8 +596,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2017-11-15</td>
           <td>Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>455</td>
-          <td>66</td>
+          <td>461</td>
+          <td>67</td>
 
             <td><a href='../recommendations/b2eb064f432557c59ce99834d7dc7817e4687271' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -616,8 +616,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2016-05-26</td>
           <td>IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE Transactions on Molecular Biological and Multi-Scale Communications</td>
-          <td>333</td>
-          <td>66</td>
+          <td>334</td>
+          <td>67</td>
 
             <td><a href='../recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -636,8 +636,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2020-04-05</td>
           <td>Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>220</td>
-          <td>66</td>
+          <td>221</td>
+          <td>67</td>
 
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@@ -656,7 +656,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2018-09-07</td>
           <td>Journal of Computational and Nonlinear Dynamics</td>
-          <td>64</td>
+          <td>65</td>
           <td>79</td>
 
             <td><a href='../recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196' target='_blank'>
@@ -677,7 +677,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           <td>2019-09-18</td>
           <td>ArXiv, arXiv.org</td>
           <td>35</td>
-          <td>66</td>
+          <td>67</td>
 
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@@ -696,8 +696,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2019-06-19</td>
           <td>Frontiers in Artificial Intelligence</td>
-          <td>75</td>
-          <td>66</td>
+          <td>77</td>
+          <td>67</td>
 
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               <i class="material-icons">open_in_new</i></a>
@@ -716,8 +716,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2016-09-21</td>
           <td>Science Advances</td>
-          <td>1229</td>
-          <td>66</td>
+          <td>1235</td>
+          <td>67</td>
 
             <td><a href='../recommendations/0acd117521ef5aafb09fed02ab415523b330b058' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -736,8 +736,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2021-11-22</td>
           <td>Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>178</td>
-          <td>66</td>
+          <td>181</td>
+          <td>67</td>
 
             <td><a href='../recommendations/883547fdbd88552328a6615ec620f96e39c57018' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -776,8 +776,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Symbolic regr
           </td>
           <td>2018-07-14</td>
           <td>IEEE Access</td>
-          <td>123</td>
-          <td>66</td>
+          <td>124</td>
+          <td>67</td>
 
             <td><a href='../recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da' target='_blank'>
               <i class="material-icons">open_in_new</i></a>
@@ -820,6 +820,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
     </thead>
     <tbody>
 
+        <tr id="Sparse Identification of Nonlinear Dynamical Systems (SINDy) is a powerful tool for the data-driven discovery of governing equations. However, it encounters challenges when modeling complex dynamical systems involving high-order derivatives or discontinuities, particularly in the presence of noise. These limitations restrict its applicability across various fields in applied mathematics and physics. To mitigate these, we propose Laplace-Enhanced SparSe Identification of Nonlinear Dynamical Systems (LES-SINDy). By transforming time-series measurements from the time domain to the Laplace domain using the Laplace transform and integration by parts, LES-SINDy enables more accurate approximations of derivatives and discontinuous terms. It also effectively handles unbounded growth functions and accumulated numerical errors in the Laplace domain, thereby overcoming challenges in the identification process. The model evaluation process selects the most accurate and parsimonious dynamical systems from multiple candidates. Experimental results across diverse ordinary and partial differential equations show that LES-SINDy achieves superior robustness, accuracy, and parsimony compared to existing methods.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/cc9d7f1e9a7f7b37237e52d46d21299f816a377c" target='_blank'>
+              LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems
+              </a>
+            </td>
+          <td>
+            Haoyang Zheng, Guang Lin
+          </td>
+          <td>2024-11-04</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
         <tr id="Identifying dynamical systems characterized by nonlinear parameters presents significant challenges in deriving mathematical models that enhance understanding of physics. Traditional methods, such as Sparse Identification of Nonlinear Dynamics (SINDy) and symbolic regression, can extract governing equations from observational data; however, they also come with distinct advantages and disadvantages. This paper introduces a novel method within the SINDy framework, termed ADAM-SINDy, which synthesizes the strengths of established approaches by employing the ADAM optimization algorithm. This facilitates the simultaneous optimization of nonlinear parameters and coefficients associated with nonlinear candidate functions, enabling precise parameter estimation without requiring prior knowledge of nonlinear characteristics such as trigonometric frequencies, exponential bandwidths, or polynomial exponents, thereby addressing a key limitation of SINDy. Through an integrated global optimization, ADAM-SINDy dynamically adjusts all unknown variables in response to data, resulting in an adaptive identification procedure that reduces the sensitivity to the library of candidate functions. The performance of the ADAM-SINDy methodology is demonstrated across a spectrum of dynamical systems, including benchmark coupled nonlinear ordinary differential equations such as oscillators, chaotic fluid flows, reaction kinetics, pharmacokinetics, as well as nonlinear partial differential equations (wildfire transport). The results demonstrate significant improvements in identifying parameterized dynamical systems and underscore the importance of concurrently optimizing all parameters, particularly those characterized by nonlinear parameters. These findings highlight the potential of ADAM-SINDy to extend the applicability of the SINDy framework in addressing more complex challenges in dynamical system identification.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/0c327ed253a2c7b658940860bcf0a9e077d634b6" target='_blank'>
@@ -847,7 +862,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2024-10-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
         <tr id="Identifying nonlinear dynamics and characterizing noise from data is critical across science and engineering for understanding and modeling the behavior of the systems accurately. The modified sparse identification of nonlinear dynamics (mSINDy) has emerged as an effective framework for identifying systems embedded in heavy noise; however, further improvements can expand its capabilities and robustness. By integrating the weak SINDy (WSINDy) into mSINDy, we introduce the weak mSINDy (WmSINDy) to improve the system identification and noise modeling by harnessing the strengths of both approaches. The proposed algorithm simultaneously identifies parsimonious nonlinear dynamics and extracts noise probability distributions using automatic differentiation. We evaluate WmSINDy using several nonlinear systems and it demonstrates improved accuracy and noise characterization over baselines for systems embedded in relatively strong noise.">
@@ -895,6 +910,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>9</td>
         </tr>
 
+        <tr id="Effectively controlling systems governed by Partial Differential Equations (PDEs) is crucial in several fields of Applied Sciences and Engineering. These systems usually yield significant challenges to conventional control schemes due to their nonlinear dynamics, partial observability, high-dimensionality once discretized, distributed nature, and the requirement for low-latency feedback control. Reinforcement Learning (RL), particularly Deep RL (DRL), has recently emerged as a promising control paradigm for such systems, demonstrating exceptional capabilities in managing high-dimensional, nonlinear dynamics. However, DRL faces challenges including sample inefficiency, robustness issues, and an overall lack of interpretability. To address these issues, we propose a data-efficient, interpretable, and scalable Dyna-style Model-Based RL framework for PDE control, combining the Sparse Identification of Nonlinear Dynamics with Control (SINDy-C) algorithm and an autoencoder (AE) framework for the sake of dimensionality reduction of PDE states and actions. This novel approach enables fast rollouts, reducing the need for extensive environment interactions, and provides an interpretable latent space representation of the PDE forward dynamics. We validate our method on two PDE problems describing fluid flows - namely, the 1D Burgers equation and 2D Navier-Stokes equations - comparing it against a model-free baseline, and carrying out an extensive analysis of the learned dynamics.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/c4afec07162a7a857a1ad05397d56c6a677f4b00" target='_blank'>
+              Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems
+              </a>
+            </td>
+          <td>
+            Florian Wolf, Nicolò Botteghi, Urban Fasel, Andrea Manzoni
+          </td>
+          <td>2024-11-06</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>11</td>
+        </tr>
+
         <tr id="This paper introduces the Parsimonious Dynamic Mode Decomposition (parsDMD), a novel algorithm designed to automatically select an optimally sparse subset of dynamic modes for both spatiotemporal and purely temporal data. By incorporating time-delay embedding and leveraging Orthogonal Matching Pursuit (OMP), parsDMD ensures robustness against noise and effectively handles complex, nonlinear dynamics. The algorithm is validated on a diverse range of datasets, including standing wave signals, identifying hidden dynamics, fluid dynamics simulations (flow past a cylinder and transonic buffet), and atmospheric sea-surface temperature (SST) data. ParsDMD addresses a significant limitation of the traditional sparsity-promoting DMD (spDMD), which requires manual tuning of sparsity parameters through a rigorous trial-and-error process to balance between single-mode and all-mode solutions. In contrast, parsDMD autonomously determines the optimally sparse subset of modes without user intervention, while maintaining minimal computational complexity. Comparative analyses demonstrate that parsDMD consistently outperforms spDMD by providing more accurate mode identification and effective reconstruction in noisy environments. These advantages render parsDMD an effective tool for real-time diagnostics, forecasting, and reduced-order model construction across various disciplines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/ababf3f8781fc0d38c9f1a58736153a9dd584068" target='_blank'>
@@ -925,19 +955,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>10</td>
         </tr>
 
-        <tr id="Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical terms. Examples where sparse optimization fails abound, such as the classic Ikeda or optical-cavity map in nonlinear dynamics and a large variety of ecosystems. Exploiting the recently articulated Kolmogorov-Arnold networks, we develop a general model-discovery framework for any dynamical systems including those that do not satisfy the sparsity condition. In particular, we demonstrate non-uniqueness in that a large number of approximate models of the system can be found which generate the same invariant set with the correct statistics such as the Lyapunov exponents and Kullback-Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.">
+        <tr id="Dynamical systems theory has long provided a foundation for understanding evolving phenomena across scientific domains. Yet, the application of this theory to complex real-world systems remains challenging due to issues in mathematical modeling, nonlinearity, and high dimensionality. In this work, we introduce a data-driven computational framework to derive low-dimensional linear models for nonlinear dynamical systems directly from raw experimental data. This framework enables global stability analysis through interpretable linear models that capture the underlying system structure. Our approach employs time-delay embedding, physics-informed deep autoencoders, and annealing-based regularization to identify novel low-dimensional coordinate representations, unlocking insights across a variety of simulated and previously unstudied experimental dynamical systems. These new coordinate representations enable accurate long-horizon predictions and automatic identification of intricate invariant sets while providing empirical stability guarantees. Our method offers a promising pathway to analyze complex dynamical behaviors across fields such as physics, climate science, and engineering, with broad implications for understanding nonlinear systems in the real world.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cf52e922a7b95ad02966f5ab74138023b406c038" target='_blank'>
-              Data-driven model discovery with Kolmogorov-Arnold networks
+          <td><a href="https://www.semanticscholar.org/paper/ac835425463bfb6a585132a06ba8fbce9643ce8d" target='_blank'>
+              Automated Global Analysis of Experimental Dynamics through Low-Dimensional Linear Embeddings
               </a>
             </td>
           <td>
-            Mohammadamin Moradi, Shirin Panahi, E. Bollt, Ying-Cheng Lai
+            Samuel A. Moore, Brian P. Mann, Boyuan Chen
           </td>
-          <td>2024-09-23</td>
+          <td>2024-11-01</td>
           <td>ArXiv</td>
-          <td>1</td>
-          <td>37</td>
+          <td>0</td>
+          <td>2</td>
         </tr>
 
         <tr id="Understanding the governing rules of complex biological systems remains a significant challenge due to the nonlinear, high-dimensional nature of biological data. In this study, we present CLERA, a novel end-to-end computational framework designed to uncover parsimonious dynamical models and identify active gene programs from single-cell RNA sequencing data. By integrating a supervised autoencoder architecture with Sparse Identification of Nonlinear Dynamics, CLERA leverages prior knowledge to simultaneously extract related low-dimensional embeddings and uncovers the underlying dynamical systems that drive the processes. Through the analysis of both synthetic and biological datasets, CLERA demonstrates robust performance in reconstructing gene expression dynamics, identifying key regulatory genes, and capturing temporal patterns across distinct cell types. CLERA’s ability to generate dynamic interaction networks, combined with network rewiring using Personalized PageRank to highlight central genes and active gene programs, offers new insights into the complex regulatory mechanisms underlying cellular processes.">
@@ -955,36 +985,51 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>8</td>
         </tr>
 
-        <tr id="The quasi-potential is a key concept in stochastic systems as it accounts for the long-term behavior of the dynamics of such systems. It also allows us to estimate mean exit times from the attractors of the system, and transition rates between states. This is of significance in many applications across various areas such as physics, biology, ecology, and economy. Computation of the quasi-potential is often obtained via a functional minimization problem that can be challenging. This paper combines a sparse learning technique with action minimization methods in order to: (i) Identify the orthogonal decomposition of the deterministic vector field (drift) driving the stochastic dynamics; (ii) Determine the quasi-potential from this decomposition. This decomposition of the drift vector field into its gradient and orthogonal parts is accomplished with the help of a machine learning-based sparse identification technique. Specifically, the so-called sparse identification of non-linear dynamics (SINDy) [1] is applied to the most likely trajectory in a stochastic system (instanton) to learn the orthogonal decomposition of the drift. Consequently, the quasi-potential can be evaluated even at points outside the instanton path, allowing our method to provide the complete quasi-potential landscape from this single trajectory. Additionally, the orthogonal drift component obtained within our framework is important as a correction to the exponential decay of transition rates and exit times. We implemented the proposed approach in 2- and 3-D systems, covering various types of potential landscapes and attractors.">
+        <tr id="Complex chaotic dynamics, seen in natural and industrial systems like turbulent flows and weather patterns, often span vast spatial domains with interactions across scales. Accurately capturing these features requires a high-dimensional state space to resolve all the time and spatial scales. For dissipative systems the dynamics lie on a finite-dimensional manifold with fewer degrees of freedom. Thus, by building reduced-order data-driven models in manifold coordinates, we can capture the essential behavior of chaotic systems. Unfortunately, these tend to be formulated globally rendering them less effective for large spatial systems. In this context, we present a data-driven low-dimensional modeling approach to tackle the complexities of chaotic motion, Markovian dynamics, multi-scale behavior, and high numbers of degrees of freedom within large spatial domains. Our methodology involves a parallel scheme of decomposing a spatially extended system into a sequence of local `patches', and constructing a set of coupled, local low-dimensional dynamical models for each patch. Here, we choose to construct the set of local models using autoencoders (for constructing the low-dimensional representation) and neural ordinary differential equations, NODE, for learning the evolution equation. Each patch, or local model, shares the same underlying functions (e.g., autoencoders and NODEs) due to the spatial homogeneity of the underlying systems we consider. We apply this method to the Kuramoto-Sivashinsky equation and 2D Kolmogorov flow, and reduce state dimension by up to two orders of magnitude while accurately capturing both short-term dynamics and long-term statistics.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5c1366ae8136e8166a061e2ccd423ceebdaa255b" target='_blank'>
-              Quasi-potential and drift decomposition in stochastic systems by sparse identification
+          <td><a href="https://www.semanticscholar.org/paper/f1d9b7cb4acfb333508b09fbb7b41c8e84bdcb2a" target='_blank'>
+              Data-driven prediction of large-scale spatiotemporal chaos with distributed low-dimensional models
               </a>
             </td>
           <td>
-            Leonardo Grigorio, Mnerh Alqahtani
+            Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
           </td>
-          <td>2024-09-10</td>
+          <td>2024-10-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Complex chaotic dynamics, seen in natural and industrial systems like turbulent flows and weather patterns, often span vast spatial domains with interactions across scales. Accurately capturing these features requires a high-dimensional state space to resolve all the time and spatial scales. For dissipative systems the dynamics lie on a finite-dimensional manifold with fewer degrees of freedom. Thus, by building reduced-order data-driven models in manifold coordinates, we can capture the essential behavior of chaotic systems. Unfortunately, these tend to be formulated globally rendering them less effective for large spatial systems. In this context, we present a data-driven low-dimensional modeling approach to tackle the complexities of chaotic motion, Markovian dynamics, multi-scale behavior, and high numbers of degrees of freedom within large spatial domains. Our methodology involves a parallel scheme of decomposing a spatially extended system into a sequence of local `patches', and constructing a set of coupled, local low-dimensional dynamical models for each patch. Here, we choose to construct the set of local models using autoencoders (for constructing the low-dimensional representation) and neural ordinary differential equations, NODE, for learning the evolution equation. Each patch, or local model, shares the same underlying functions (e.g., autoencoders and NODEs) due to the spatial homogeneity of the underlying systems we consider. We apply this method to the Kuramoto-Sivashinsky equation and 2D Kolmogorov flow, and reduce state dimension by up to two orders of magnitude while accurately capturing both short-term dynamics and long-term statistics.">
+        <tr id="We propose the use of the Extended Kalman Filter (EKF) for online data assimilation and update of a dynamic model, preliminary identified through the Sparse Identification of Nonlinear Dynamics (SINDy). This data-driven technique may avoid biases due to incorrect modelling assumptions and exploits SINDy to approximate the system dynamics leveraging a predefined library of functions, where active terms are selected and weighted by a sparse set of coefficients. This results in a physically-sound and interpretable dynamic model allowing to reduce epistemic uncertainty often affecting machine learning approaches. Treating the SINDy model coefficients as random variables, we propose to update them while acquiring (possibly noisy) system measurements, thus enabling the online identification of time-varying systems. These changes can stem from, e.g., varying operational conditions or unforeseen events. The EKF performs model adaptation through joint state-parameters estimation, with the Jacobian matrices required to computed the model sensitivity inexpensively evaluated from the SINDy model formulation. The effectiveness of this approach is demonstrated through three case studies: (i) a Lokta-Volterra model in which all parameters simultaneously evolve during the observation period; (ii) a Selkov model where the system undergoes a bifurcation not seen during the SINDy training; (iii) a MEMS arch exhibiting a 1:2 internal resonance. The ability of EKF of recovering inactivated functional terms from the SINDy library, or discarding unnecessary contribution, is also highlighted. Based on the presented applications, this method shows strong promise for handling time-varying nonlinear dynamic systems possibly experiencing bifurcating behaviours.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f1d9b7cb4acfb333508b09fbb7b41c8e84bdcb2a" target='_blank'>
-              Data-driven prediction of large-scale spatiotemporal chaos with distributed low-dimensional models
+          <td><a href="https://www.semanticscholar.org/paper/2d108c654cd0caef592cad2586543c394a27e334" target='_blank'>
+              Online learning in bifurcating dynamic systems via SINDy and Kalman filtering
               </a>
             </td>
           <td>
-            Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham
+            Luca Rosafalco, Paolo Conti, Andrea Manzoni, Stefano Mariani, A. Frangi
           </td>
-          <td>2024-10-02</td>
+          <td>2024-11-07</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>8</td>
         </tr>
 
+        <tr id="Data-driven model discovery of complex dynamical systems is typically done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical terms. Examples where sparse optimization fails abound, such as the classic Ikeda or optical-cavity map in nonlinear dynamics and a large variety of ecosystems. Exploiting the recently articulated Kolmogorov-Arnold networks, we develop a general model-discovery framework for any dynamical systems including those that do not satisfy the sparsity condition. In particular, we demonstrate non-uniqueness in that a large number of approximate models of the system can be found which generate the same invariant set with the correct statistics such as the Lyapunov exponents and Kullback-Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/cf52e922a7b95ad02966f5ab74138023b406c038" target='_blank'>
+              Data-driven model discovery with Kolmogorov-Arnold networks
+              </a>
+            </td>
+          <td>
+            Mohammadamin Moradi, Shirin Panahi, E. Bollt, Ying-Cheng Lai
+          </td>
+          <td>2024-09-23</td>
+          <td>ArXiv</td>
+          <td>2</td>
+          <td>37</td>
+        </tr>
+
         <tr id="Discovering explicit governing equations of stochastic dynamical systems with both (Gaussian) Brownian noise and (non-Gaussian) L\'evy noise from data is chanllenging due to possible intricate functional forms and the inherent complexity of L\'evy motion. This present research endeavors to develop an evolutionary symbol sparse regression (ESSR) approach to extract non-Gaussian stochastic dynamical systems from sample path data, based on nonlocal Kramers-Moyal formulas, genetic programming, and sparse regression. More specifically, the genetic programming is employed to generate a diverse array of candidate functions, the sparse regression technique aims at learning the coefficients associated with these candidates, and the nonlocal Kramers-Moyal formulas serve as the foundation for constructing the fitness measure in genetic programming and the loss function in sparse regression. The efficacy and capabilities of this approach are showcased through its application to several illustrative models. This approach stands out as a potent instrument for deciphering non-Gaussian stochastic dynamics from available datasets, indicating a wide range of applications across different fields.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/92a8fe5ae6040e98e4e335d324524edb1ada08a5" target='_blank'>
@@ -1000,6 +1045,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>0</td>
         </tr>
 
+        <tr id="Motivated by the surge of interest in Koopman operator theory, we propose a machine-learning alternative based on a functional Bayesian perspective for operator-theoretic modeling of unknown, data-driven, nonlinear dynamical systems. This formulation is directly done in an infinite-dimensional space of linear operators or Hilbert space with universal approximation property. The theory of reproducing kernel Hilbert space (RKHS) allows the lifting of nonlinear dynamics to a potentially infinite-dimensional space via linear embeddings, where a general nonlinear function is represented as a set of linear functions or operators in the functional space. This allows us to apply classical linear Bayesian methods such as the Kalman filter directly in the Hilbert space, yielding nonlinear solutions in the original input space. This kernel perspective on the Koopman operator offers two compelling advantages. First, the Hilbert space can be constructed deterministically, agnostic to the nonlinear dynamics. The Gaussian kernel is universal, approximating uniformly an arbitrary continuous target function over any compact domain. Second, Bayesian filter is an adaptive, linear minimum-variance algorithm, allowing the system to update the Koopman operator and continuously track the changes across an extended period of time, ideally suited for modern data-driven applications such as real-time machine learning using streaming data. In this paper, we present several practical implementations to obtain a finite-dimensional approximation of the functional Bayesian filter (FBF). Due to the rapid decay of the Gaussian kernel, excellent approximation is obtained with a small dimension. We demonstrate that this practical approach can obtain accurate results and outperform finite-dimensional Koopman decomposition.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/c6f90946466ada48fed9585f5ab005d325d95a21" target='_blank'>
+              Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical Systems
+              </a>
+            </td>
+          <td>
+            Kan Li, Jos'e C. Pr'incipe
+          </td>
+          <td>2024-10-31</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>2</td>
+        </tr>
+
         <tr id="Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents significant challenges for carrying out simultaneous data assimilation (DA), which is a crucial technique for state estimation, model identification, and reconstructing missing data. Integrating ensemble-based DA methods with nonlinear deep learning models is computationally expensive and may suffer from large sampling errors. To address these challenges, we introduce a deep learning framework designed to simultaneously provide accurate forecasts and efficient DA. It is named Conditional Gaussian Koopman Network (CGKN), which transforms general nonlinear systems into nonlinear neural differential equations with conditional Gaussian structures. CGKN aims to retain essential nonlinear components while applying systematic and minimal simplifications to facilitate the development of analytic formulae for nonlinear DA. This allows for seamless integration of DA performance into the deep learning training process, eliminating the need for empirical tuning as required in ensemble methods. CGKN compensates for structural simplifications by lifting the dimension of the system, which is motivated by Koopman theory. Nevertheless, CGKN exploits special nonlinear dynamics within the lifted space. This enables the model to capture extreme events and strong non-Gaussian features in joint and marginal distributions with appropriate uncertainty quantification. We demonstrate the effectiveness of CGKN for both prediction and DA on three strongly nonlinear and non-Gaussian turbulent systems: the projected stochastic Burgers--Sivashinsky equation, the Lorenz 96 system, and the El Ni\~no-Southern Oscillation. The results justify the robustness and computational efficiency of CGKN.">
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           <td><a href="https://www.semanticscholar.org/paper/394d2bab1a7b169a1a96f765eea12d6aad03bac4" target='_blank'>
@@ -1030,6 +1090,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2</td>
         </tr>
 
+        <tr id="Ultrafast optics is driven by a myriad of complex nonlinear dynamics. The ubiquitous presence of governing equations in the form of partial integro-differential equations (PIDE) necessitates the need for advanced computational tools to understand the underlying physical mechanisms. From the experimental perspective, signal-to-noise ratio and availability of measurable data, accounts for a bottle neck in numerical and data-driven modeling methods. In this paper we extend the application of the physics informed neural network (PINN) architecture to include prior knowledge in both the physical and Fourier domain. We demonstrate our Fourier Domain PINN (FD-PINN) in two distinct forms. The Continuous time FD-PINN is used to predict accurate solutions to the Generalized Pulse Propagation Equation, which includes the complete delayed nonlinear response, in the data-starved and noisy regime. We extend the architecture to the Discrete time FD-PINN to recover the delayed-response physics from spatially separated measurement points. We conducted the first systematic study of the effect of SNR on the spatiotemporal field prediction as well as physics discovery. Our architecture ensures high fidelity predictive modeling and hidden physics recovery for applications such as image reconstruction, pulse characterization and shaping, as well as hidden parameter discovery. The benefits of the FD-PINN for ultrafast nonlinear optics make it immediately experimentally deployable. FD-PINN represents the next generation of tools to study optical phenomena both through modeling and measurements for both forward and inverse problems.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/83e7a6c9c52d8790d3533dd032d3b73d1802c725" target='_blank'>
+              Fourier Domain Physics Informed Neural Network
+              </a>
+            </td>
+          <td>
+            Jonathan Musgrave, Shu-Wei Huang
+          </td>
+          <td>2024-09-30</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
+        </tr>
+
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/037d5b01fa6bf3a903830fd8399f38895b65e5c8" target='_blank'>
@@ -1045,6 +1120,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>23</td>
         </tr>
 
+        <tr id="Dynamical systems form the foundation of scientific discovery, traditionally modeled with predefined state variables such as the angle and angular velocity, and differential equations such as the equation of motion for a single pendulum. We propose an approach to discover a set of state variables that preserve the smoothness of the system dynamics and to construct a vector field representing the system's dynamics equation, automatically from video streams without prior physical knowledge. The prominence and effectiveness of the proposed approach are demonstrated through both quantitative and qualitative analyses of various dynamical systems, including the prediction of characteristic frequencies and the identification of chaotic and limit cycle behaviors. This shows the potential of our approach to assist human scientists in scientific discovery.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/6e5457c98e86e80dc7ee5217c3b0e18000ab5348" target='_blank'>
+              Automated Discovery of Continuous Dynamics from Videos
+              </a>
+            </td>
+          <td>
+            Kuang Huang, Dong Heon Cho, Boyuan Chen
+          </td>
+          <td>2024-10-14</td>
+          <td>ArXiv</td>
+          <td>1</td>
+          <td>0</td>
+        </tr>
+
         <tr id="Partial differential equations, and their chaotic solutions, are pervasive in the modelling of complex systems in engineering, science, and beyond. Data-driven methods can find solutions to partial differential equations with a divide-and-conquer strategy: The solution is sought in a latent space, on which the temporal dynamics are inferred (``latent-space'' approach). This is achieved by, first, compressing the data with an autoencoder, and, second, inferring the temporal dynamics with recurrent neural networks. The overarching goal of this paper is to show that a latent-space approach can not only infer the solution of a chaotic partial differential equation, but it can also predict the stability properties of the physical system. First, we employ the convolutional autoencoder echo state network (CAE-ESN) on the chaotic Kuramoto-Sivashinsky equation for various chaotic regimes. We show that the CAE-ESN (i) finds a low-dimensional latent-space representation of the observations and (ii) accurately infers the Lyapunov exponents and covariant Lyapunov vectors (CLVs) in this low-dimensional manifold for different attractors. Second, we extend the CAE-ESN to a turbulent flow, comparing the Lyapunov spectrum to estimates obtained from Jacobian-free methods. A latent-space approach based on the CAE-ESN effectively produces a latent space that preserves the key properties of the chaotic system, such as Lyapunov exponents and CLVs, thus retaining the geometric structure of the attractor. The latent-space approach based on the CAE-ESN is a reduced-order model that accurately predicts the dynamics of the chaotic system, or, alternatively, it can be used to infer stability properties of chaotic systems from data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/40a24ed82c56c25b5f7fc0acaad378e756d9644b" target='_blank'>
@@ -1120,34 +1210,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>5</td>
         </tr>
 
-        <tr id="Modeling and controlling complex spatiotemporal dynamical systems driven by partial differential equations (PDEs) often necessitate dimensionality reduction techniques to construct lower-order models for computational efficiency. This paper explores a deep autoencoding learning method for reduced-order modeling and control of dynamical systems governed by spatiotemporal PDEs. We first analytically show that an optimization objective for learning a linear autoencoding reduced-order model can be formulated to yield a solution closely resembling the result obtained through the dynamic mode decomposition with control algorithm. We then extend this linear autoencoding architecture to a deep autoencoding framework, enabling the development of a nonlinear reduced-order model. Furthermore, we leverage the learned reduced-order model to design controllers using stability-constrained deep neural networks. Numerical experiments are presented to validate the efficacy of our approach in both modeling and control using the example of a reaction-diffusion system.">
+        <tr id="The celebrated Takens' embedding theorem provides a theoretical foundation for reconstructing the full state of a dynamical system from partial observations. However, the classical theorem assumes that the underlying system is deterministic and that observations are noise-free, limiting its applicability in real-world scenarios. Motivated by these limitations, we rigorously establish a measure-theoretic generalization that adopts an Eulerian description of the dynamics and recasts the embedding as a pushforward map between probability spaces. Our mathematical results leverage recent advances in optimal transportation theory. Building on our novel measure-theoretic time-delay embedding theory, we have developed a new computational framework that forecasts the full state of a dynamical system from time-lagged partial observations, engineered with better robustness to handle sparse and noisy data. We showcase the efficacy and versatility of our approach through several numerical examples, ranging from the classic Lorenz-63 system to large-scale, real-world applications such as NOAA sea surface temperature forecasting and ERA5 wind field reconstruction.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/550297187ed813182eac5e6799e3f82cf376e9dc" target='_blank'>
-              Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs
+          <td><a href="https://www.semanticscholar.org/paper/005c02e2fb7a7bc5cbe28f120e73daaf0c9f2dcc" target='_blank'>
+              Measure-Theoretic Time-Delay Embedding
               </a>
             </td>
           <td>
-            Priyabrata Saha, Saibal Mukhopadhyay
+            Jonah Botvinick-Greenhouse, Maria Oprea, R. Maulik, Yunan Yang
           </td>
-          <td>2024-09-09</td>
+          <td>2024-09-13</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>7</td>
+          <td>22</td>
         </tr>
 
-        <tr id="The celebrated Takens' embedding theorem provides a theoretical foundation for reconstructing the full state of a dynamical system from partial observations. However, the classical theorem assumes that the underlying system is deterministic and that observations are noise-free, limiting its applicability in real-world scenarios. Motivated by these limitations, we rigorously establish a measure-theoretic generalization that adopts an Eulerian description of the dynamics and recasts the embedding as a pushforward map between probability spaces. Our mathematical results leverage recent advances in optimal transportation theory. Building on our novel measure-theoretic time-delay embedding theory, we have developed a new computational framework that forecasts the full state of a dynamical system from time-lagged partial observations, engineered with better robustness to handle sparse and noisy data. We showcase the efficacy and versatility of our approach through several numerical examples, ranging from the classic Lorenz-63 system to large-scale, real-world applications such as NOAA sea surface temperature forecasting and ERA5 wind field reconstruction.">
+        <tr id="In recent years, live-cell imaging has generated detailed spatiotemporal datasets of biochemical networks within cells. These networks often exhibit characteristics of spatially-distributed excitable systems, with propagating waves of signaling activity that govern processes such as cell migration, division, and other essential physiological functions. Traditionally, these reaction-diffusion systems have been modeled using stochastic partial differential equations incorporating spatial Langevin-type dynamics. Although these knowledge-based models have provided valuable insights, they are typically not directly inferred from experimental data. In this study, we introduce and apply two data-driven methodologies for learning the structure and parameters of spatial Langevin equation (SLE) models: 1) Kramers-Moyal regression, an adaptation of an established approach to fit microscale stochastic dynamics, and 2) wave features optimization, a novel approach to match macroscale spatiotemporal patterns. Unlike black-box neural network models that predict system behavior but provide no mechanistic insight, this approach estimates nonlinear model equations that directly relate to system structure and dynamics. As a proof-of-concept, we focus on simulation datasets derived from two stochastic reaction-diffusion models: one based on the FitzHugh-Nagumo equations (FHN model) and another on a biochemically adapted version of the FHN equations (FR model). Our results demonstrate that optimizing stochastic dynamics and wave patterns enables the accurate estimation of SLE model structure and parameters directly from 1D and 2D spatial data. With sparsity enforcement, we also identify novel sparse reaction-diffusion models for excitable systems. We show that this approach effectively approximates system behavior even when working with datasets similar to experimental conditions, with low temporal resolution and unobserved molecular components. By leveraging machine learning techniques for robust estimation of excitable reaction-diffusion models from spatiotemporal data, this work enhances our ability to model and understand the complex systems that regulate cell behavior.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/005c02e2fb7a7bc5cbe28f120e73daaf0c9f2dcc" target='_blank'>
-              Measure-Theoretic Time-Delay Embedding
+          <td><a href="https://www.semanticscholar.org/paper/2476518d1b9e5bf079d4defed66b90fe1272970f" target='_blank'>
+              Machine learning sparse reaction-diffusion models from stochastic dynamics and spatiotemporal patterns
               </a>
             </td>
           <td>
-            Jonah Botvinick-Greenhouse, Maria Oprea, R. Maulik, Yunan Yang
+            Bedri Abubaker-Sharif, Peter N. Devreotes, Pablo A. Iglesias
           </td>
-          <td>2024-09-13</td>
-          <td>ArXiv</td>
+          <td>2024-10-03</td>
+          <td>bioRxiv</td>
           <td>0</td>
-          <td>22</td>
+          <td>4</td>
         </tr>
 
         <tr id="In climate science, models for global warming and weather prediction face significant challenges due to the limited availability of high-quality data and the difficulty in obtaining it, making data efficiency crucial. In the past few years, Scientific Machine Learning (SciML) models have gained tremendous traction as they can be trained in a data-efficient manner, making them highly suitable for real-world climate applications. Despite this, very little attention has been paid to chaotic climate system modeling utilizing SciML methods. In this paper, we have integrated SciML methods into foundational weather models, where we have enhanced large-scale climate predictions with a physics-informed approach that achieves high accuracy with reduced data. We successfully demonstrate that by combining the interpretability of physical climate models with the computational power of neural networks, SciML models can prove to be a reliable tool for modeling climate. This indicates a shift from the traditional black box-based machine learning modeling of climate systems to physics-informed decision-making, leading to effective climate policy implementation.">
@@ -1165,19 +1255,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>3</td>
         </tr>
 
-        <tr id="In recent years, live-cell imaging has generated detailed spatiotemporal datasets of biochemical networks within cells. These networks often exhibit characteristics of spatially-distributed excitable systems, with propagating waves of signaling activity that govern processes such as cell migration, division, and other essential physiological functions. Traditionally, these reaction-diffusion systems have been modeled using stochastic partial differential equations incorporating spatial Langevin-type dynamics. Although these knowledge-based models have provided valuable insights, they are typically not directly inferred from experimental data. In this study, we introduce and apply two data-driven methodologies for learning the structure and parameters of spatial Langevin equation (SLE) models: 1) Kramers-Moyal regression, an adaptation of an established approach to fit microscale stochastic dynamics, and 2) wave features optimization, a novel approach to match macroscale spatiotemporal patterns. Unlike black-box neural network models that predict system behavior but provide no mechanistic insight, this approach estimates nonlinear model equations that directly relate to system structure and dynamics. As a proof-of-concept, we focus on simulation datasets derived from two stochastic reaction-diffusion models: one based on the FitzHugh-Nagumo equations (FHN model) and another on a biochemically adapted version of the FHN equations (FR model). Our results demonstrate that optimizing stochastic dynamics and wave patterns enables the accurate estimation of SLE model structure and parameters directly from 1D and 2D spatial data. With sparsity enforcement, we also identify novel sparse reaction-diffusion models for excitable systems. We show that this approach effectively approximates system behavior even when working with datasets similar to experimental conditions, with low temporal resolution and unobserved molecular components. By leveraging machine learning techniques for robust estimation of excitable reaction-diffusion models from spatiotemporal data, this work enhances our ability to model and understand the complex systems that regulate cell behavior.">
+        <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2476518d1b9e5bf079d4defed66b90fe1272970f" target='_blank'>
-              Machine learning sparse reaction-diffusion models from stochastic dynamics and spatiotemporal patterns
+          <td><a href="https://www.semanticscholar.org/paper/382e068c61d06d6556fc2d6a44e2bd464486a883" target='_blank'>
+              Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning
               </a>
             </td>
           <td>
-            Bedri Abubaker-Sharif, Peter N. Devreotes, Pablo A. Iglesias
+            Y. M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee, S. Datta, J. M. Bello-Rivas, Ioannis G Kevrekidis
           </td>
-          <td>2024-10-03</td>
-          <td>bioRxiv</td>
+          <td>2024-10-24</td>
+          <td>BMC Bioinformatics</td>
           <td>0</td>
-          <td>4</td>
+          <td>35</td>
+        </tr>
+
+        <tr id="Solving partial differential equations (PDEs) is essential in scientific forecasting and fluid dynamics. Traditional approaches often incur expensive computational costs and trade-offs in efficiency and accuracy. Recent deep neural networks improve accuracy but require quality training data. Physics-informed neural networks (PINNs) effectively integrate physical laws, reducing data reliance in limited sample scenarios. A novel machine-learning framework, Chebyshev physics-informed Kolmogorov-Arnold network (ChebPIKAN), is proposed to integrate the robust architectures of Kolmogorov-Arnold networks (KAN) with physical constraints to enhance calculation accuracy of PDEs for fluid mechanics. We explore the fundamentals of KAN, emphasis on the advantages of using the orthogonality of Chebyshev polynomial basis functions in spline fitting, and describe the incorporation of physics-informed loss functions tailored to specific PDEs in fluid dynamics, including Allen-Cahn equation, nonlinear Burgers equation, two-dimensional Helmholtz equations, two-dimensional Kovasznay flow and two-dimensional Navier-Stokes equations. Extensive experiments demonstrate that the proposed ChebPIKAN model significantly outperforms standard KAN architecture in solving various PDEs by embedding essential physical information more effectively. These results indicate that augmenting KAN with physical constraints can not only alleviate overfitting issues of KAN but also improve extrapolation performance. Consequently, this study highlights the potential of ChebPIKAN as a powerful tool in computational fluid dynamics, proposing a path toward fast and reliable predictions in fluid mechanics and beyond.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/4dd0f46fe2d56b8d6854361349a8968b87261e10" target='_blank'>
+              Physics-informed Kolmogorov-Arnold Network with Chebyshev Polynomials for Fluid Mechanics
+              </a>
+            </td>
+          <td>
+            Chunyu Guo, Lucheng Sun, Shilong Li, Zelong Yuan, Chao Wang
+          </td>
+          <td>2024-11-07</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>1</td>
         </tr>
 
         <tr id="In an earlier work by a subset of the present authors, the method of the so-called neural deflation was introduced towards identifying a complete set of functionally independent conservation laws of a nonlinear dynamical system. Here, we extend by a significant step this proposal. Instead of using the explicit knowledge of the underlying equations of motion, we develop the method directly from system trajectories. This is crucial towards enhancing the practical implementation of the method in scenarios where solely data reflecting discrete snapshots of the system are available. We showcase the results of the method and the number of associated conservation laws obtained in a diverse range of examples including 1D and 2D harmonic oscillators, the Toda lattice, the Fermi-Pasta-Ulam-Tsingou lattice and the Calogero-Moser system.">
@@ -1225,34 +1330,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>2</td>
         </tr>
 
-        <tr id="Dynamical systems (DS) theory is fundamental for many areas of science and engineering. It can provide deep insights into the behavior of systems evolving in time, as typically described by differential or recursive equations. A common approach to facilitate mathematical tractability and interpretability of DS models involves decomposing nonlinear DS into multiple linear DS separated by switching manifolds, i.e. piecewise linear (PWL) systems. PWL models are popular in engineering and a frequent choice in mathematics for analyzing the topological properties of DS. However, hand-crafting such models is tedious and only possible for very low-dimensional scenarios, while inferring them from data usually gives rise to unnecessarily complex representations with very many linear subregions. Here we introduce Almost-Linear Recurrent Neural Networks (AL-RNNs) which automatically and robustly produce most parsimonious PWL representations of DS from time series data, using as few PWL nonlinearities as possible. AL-RNNs can be efficiently trained with any SOTA algorithm for dynamical systems reconstruction (DSR), and naturally give rise to a symbolic encoding of the underlying DS that provably preserves important topological properties. We show that for the Lorenz and R\"ossler systems, AL-RNNs discover, in a purely data-driven way, the known topologically minimal PWL representations of the corresponding chaotic attractors. We further illustrate on two challenging empirical datasets that interpretable symbolic encodings of the dynamics can be achieved, tremendously facilitating mathematical and computational analysis of the underlying systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cdc4fd1deba6b38640d46f9dd4a912ffaa31ea25" target='_blank'>
-              Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction
-              </a>
-            </td>
-          <td>
-            Manuel Brenner, Christoph Jurgen Hemmer, Z. Monfared, Daniel Durstewitz
-          </td>
-          <td>2024-10-18</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>7</td>
-        </tr>
-
-        <tr id="Many important physical systems can be described as the evolution of a Hamiltonian system, which has the important property of being conservative, that is, energy is conserved throughout the evolution. Physics Informed Neural Networks and in particular Hamiltonian Neural Networks have emerged as a mechanism to incorporate structural inductive bias into the NN model. By ensuring physical invariances are conserved, the models exhibit significantly better sample complexity and out-of-distribution accuracy than standard NNs. Learning the Hamiltonian as a function of its canonical variables, typically position and velocity, from sample observations of the system thus becomes a critical task in system identification and long-term prediction of system behavior. However, to truly preserve the long-run physical conservation properties of Hamiltonian systems, one must use symplectic integrators for a forward pass of the system's simulation. While symplectic schemes have been used in the literature, they are thus far limited to situations when they reduce to explicit algorithms, which include the case of separable Hamiltonians or augmented non-separable Hamiltonians. We extend it to generalized non-separable Hamiltonians, and noting the self-adjoint property of symplectic integrators, we bypass computationally intensive backpropagation through an ODE solver. We show that the method is robust to noise and provides a good approximation of the system Hamiltonian when the state variables are sampled from a noisy observation. In the numerical results, we show the performance of the method concerning Hamiltonian reconstruction and conservation, indicating its particular advantage for non-separable systems.">
+        <tr id="The reconstruction of physical fields from sparse measurements is pivotal in both scientific research and engineering applications. Traditional methods are increasingly supplemented by deep learning models due to their efficacy in extracting features from data. However, except for the low accuracy on complex physical systems, these models often fail to comply with essential physical constraints, such as governing equations and boundary conditions. To overcome this limitation, we introduce a novel data-driven field reconstruction framework, termed the Physics-aligned Schr\"{o}dinger Bridge (PalSB). This framework leverages a diffusion Schr\"{o}dinger bridge mechanism that is specifically tailored to align with physical constraints. The PalSB approach incorporates a dual-stage training process designed to address both local reconstruction mapping and global physical principles. Additionally, a boundary-aware sampling technique is implemented to ensure adherence to physical boundary conditions. We demonstrate the effectiveness of PalSB through its application to three complex nonlinear systems: cylinder flow from Particle Image Velocimetry experiments, two-dimensional turbulence, and a reaction-diffusion system. The results reveal that PalSB not only achieves higher accuracy but also exhibits enhanced compliance with physical constraints compared to existing methods. This highlights PalSB's capability to generate high-quality representations of intricate physical interactions, showcasing its potential for advancing field reconstruction techniques.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e92316bb21fc0f2dba7399ed2b4dceedd3feac93" target='_blank'>
-              Learning Generalized Hamiltonians using fully Symplectic Mappings
+          <td><a href="https://www.semanticscholar.org/paper/27d02977437f60b3287aedfb018f690ee79b8b62" target='_blank'>
+              Physics-aligned Schrödinger bridge
               </a>
             </td>
           <td>
-            Harsh Choudhary, Chandan Gupta, Vyacheslav kungrutsev, Melvin Leok, Georgios Korpas
+            Zeyu Li, Hongkun Dou, Shen Fang, Wang Han, Yue Deng, Lijun Yang
           </td>
-          <td>2024-09-17</td>
+          <td>2024-09-26</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>2</td>
         </tr>
 
         <tr id="Predicting the response of nonlinear dynamical systems subject to random, broadband excitation is important across a range of scientific disciplines, such as structural dynamics and neuroscience. Building data-driven models requires experimental measurements of the system input and output, but it can be difficult to determine whether inaccuracies in the model stem from modelling errors or noise. This paper presents a novel method to identify the causal component of the input-output data from measurements of a system in the presence of output noise, as a function of frequency, without needing a high fidelity model. An output prediction, calculated using an available model, is optimally combined with noisy measurements of the output to predict the input to the system. The parameters of the algorithm balance the two output signals and are utilised to calculate a nonlinear coherence metric as a measure of causality. This method is applicable to a broad class of nonlinear dynamical systems. There are currently no solutions to this problem in the absence of a complete benchmark model.">
@@ -1270,34 +1360,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>1</td>
         </tr>
 
-        <tr id="Efficiently estimating system dynamics from data is essential for minimizing data collection costs and improving model performance. This work addresses the challenge of designing future control inputs to maximize information gain, thereby improving the efficiency of the system identification process. We propose an approach that integrates informative input design into the Dynamic Mode Decomposition with control (DMDc) framework, which is well-suited for high-dimensional systems. By formulating an approximate convex optimization problem that minimizes the trace of the estimation error covariance matrix, we are able to efficiently reduce uncertainty in the model parameters while respecting constraints on the system states and control inputs. This method outperforms traditional techniques like Pseudo-Random Binary Sequences (PRBS) and orthogonal multisines, which do not adapt to the current system model and often gather redundant information. We validate our approach using aircraft and fluid dynamics simulations to demonstrate the practical applicability and effectiveness of our method. Our results show that strategically planning control inputs based on the current model enhances the accuracy of system identification while requiring less data. Furthermore, we provide our implementation and simulation interfaces as an open-source software package, facilitating further research development and use by industry practitioners.">
+        <tr id="Dynamical systems (DS) theory is fundamental for many areas of science and engineering. It can provide deep insights into the behavior of systems evolving in time, as typically described by differential or recursive equations. A common approach to facilitate mathematical tractability and interpretability of DS models involves decomposing nonlinear DS into multiple linear DS separated by switching manifolds, i.e. piecewise linear (PWL) systems. PWL models are popular in engineering and a frequent choice in mathematics for analyzing the topological properties of DS. However, hand-crafting such models is tedious and only possible for very low-dimensional scenarios, while inferring them from data usually gives rise to unnecessarily complex representations with very many linear subregions. Here we introduce Almost-Linear Recurrent Neural Networks (AL-RNNs) which automatically and robustly produce most parsimonious PWL representations of DS from time series data, using as few PWL nonlinearities as possible. AL-RNNs can be efficiently trained with any SOTA algorithm for dynamical systems reconstruction (DSR), and naturally give rise to a symbolic encoding of the underlying DS that provably preserves important topological properties. We show that for the Lorenz and R\"ossler systems, AL-RNNs discover, in a purely data-driven way, the known topologically minimal PWL representations of the corresponding chaotic attractors. We further illustrate on two challenging empirical datasets that interpretable symbolic encodings of the dynamics can be achieved, tremendously facilitating mathematical and computational analysis of the underlying systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/6127ef757da53faa534ca8a3e197c3eefe94e5c0" target='_blank'>
-              Informative Input Design for Dynamic Mode Decomposition
+          <td><a href="https://www.semanticscholar.org/paper/cdc4fd1deba6b38640d46f9dd4a912ffaa31ea25" target='_blank'>
+              Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction
               </a>
             </td>
           <td>
-            Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd
+            Manuel Brenner, Christoph Jurgen Hemmer, Z. Monfared, Daniel Durstewitz
           </td>
-          <td>2024-09-19</td>
+          <td>2024-10-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>17</td>
+          <td>7</td>
         </tr>
 
-        <tr id="Steering a system towards a desired target in a very short amount of time is challenging from a computational standpoint. Indeed, the intrinsically iterative nature of optimal control problems requires multiple simulations of the physical system to be controlled. Moreover, the control action needs to be updated whenever the underlying scenario undergoes variations. Full-order models based on, e.g., the Finite Element Method, do not meet these requirements due to the computational burden they usually entail. On the other hand, conventional reduced order modeling techniques such as the Reduced Basis method, are intrusive, rely on a linear superimposition of modes, and lack of efficiency when addressing nonlinear time-dependent dynamics. In this work, we propose a non-intrusive Deep Learning-based Reduced Order Modeling (DL-ROM) technique for the rapid control of systems described in terms of parametrized PDEs in multiple scenarios. In particular, optimal full-order snapshots are generated and properly reduced by either Proper Orthogonal Decomposition or deep autoencoders (or a combination thereof) while feedforward neural networks are exploited to learn the map from scenario parameters to reduced optimal solutions. Nonlinear dimensionality reduction therefore allows us to consider state variables and control actions that are both low-dimensional and distributed. After (i) data generation, (ii) dimensionality reduction, and (iii) neural networks training in the offline phase, optimal control strategies can be rapidly retrieved in an online phase for any scenario of interest. The computational speedup and the high accuracy obtained with the proposed approach are assessed on different PDE-constrained optimization problems, ranging from the minimization of energy dissipation in incompressible flows modelled through Navier-Stokes equations to the thermal active cooling in heat transfer.">
+        <tr id="Many important physical systems can be described as the evolution of a Hamiltonian system, which has the important property of being conservative, that is, energy is conserved throughout the evolution. Physics Informed Neural Networks and in particular Hamiltonian Neural Networks have emerged as a mechanism to incorporate structural inductive bias into the NN model. By ensuring physical invariances are conserved, the models exhibit significantly better sample complexity and out-of-distribution accuracy than standard NNs. Learning the Hamiltonian as a function of its canonical variables, typically position and velocity, from sample observations of the system thus becomes a critical task in system identification and long-term prediction of system behavior. However, to truly preserve the long-run physical conservation properties of Hamiltonian systems, one must use symplectic integrators for a forward pass of the system's simulation. While symplectic schemes have been used in the literature, they are thus far limited to situations when they reduce to explicit algorithms, which include the case of separable Hamiltonians or augmented non-separable Hamiltonians. We extend it to generalized non-separable Hamiltonians, and noting the self-adjoint property of symplectic integrators, we bypass computationally intensive backpropagation through an ODE solver. We show that the method is robust to noise and provides a good approximation of the system Hamiltonian when the state variables are sampled from a noisy observation. In the numerical results, we show the performance of the method concerning Hamiltonian reconstruction and conservation, indicating its particular advantage for non-separable systems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/915f70178748c7ef3d789390c868584eaf1f8481" target='_blank'>
-              Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
+          <td><a href="https://www.semanticscholar.org/paper/e92316bb21fc0f2dba7399ed2b4dceedd3feac93" target='_blank'>
+              Learning Generalized Hamiltonians using fully Symplectic Mappings
               </a>
             </td>
           <td>
-            Matteo Tomasetto, Andrea Manzoni, Francesco Braghin
+            Harsh Choudhary, Chandan Gupta, Vyacheslav kungrutsev, Melvin Leok, Georgios Korpas
           </td>
-          <td>2024-09-09</td>
+          <td>2024-09-17</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
+          <td>0</td>
         </tr>
 
         <tr id="Chaotic dynamics, commonly seen in weather systems and fluid turbulence, are characterized by their sensitivity to initial conditions, which makes accurate prediction challenging. Despite its sensitivity to initial perturbations, many chaotic systems observe dissipative behaviors and ergodicity. Therefore, recently various approaches have been proposed to develop data-driven models preserving invariant statistics over long horizons. Although these methods have shown empirical success in reducing instances of unbounded trajectory generation, many of the models are still prone to generating unbounded trajectories, leading to invalid statistics evaluation. In this paper, we propose a novel neural network architecture that simultaneously learns a dissipative dynamics emulator that guarantees to generate bounded trajectories and an energy-like function that governs the dissipative behavior. More specifically, by leveraging control-theoretic ideas, we derive algebraic conditions based on the learned energy-like function that ensure asymptotic convergence to an invariant level set. Using these algebraic conditions, our proposed model enforces dissipativity through a ReLU projection layer, which provides formal trajectory boundedness guarantees. Furthermore, the invariant level set provides an outer estimate for the strange attractor, which is known to be very difficult to characterize due to its complex geometry. We demonstrate the capability of our model in producing bounded long-horizon trajectory forecasts and characterizing the attractor for chaotic dynamical systems including Lorenz 96 and a truncated Kuramoto-Sivashinsky equation.">
@@ -1315,229 +1405,139 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
           <td>39</td>
         </tr>
 
-        <tr id="We consider the task of data-driven identification of dynamical systems, specifically for systems whose behavior at large frequencies is non-standard, as encoded by a non-trivial relative degree of the transfer function or, alternatively, a non-trivial index of a corresponding realization as a descriptor system. We develop novel surrogate modeling strategies that allow state-of-the-art rational approximation algorithms (e.g., AAA and vector fitting) to better handle data coming from such systems with non-trivial relative degree. Our contribution is twofold. On one hand, we describe a strategy to build rational surrogate models with prescribed relative degree, with the objective of mirroring the high-frequency behavior of the high-fidelity problem, when known. The surrogate model's desired degree is achieved through constraints on its barycentric coefficients, rather than through ad-hoc modifications of the rational form. On the other hand, we present a degree-identification routine that allows one to estimate the unknown relative degree of a system from low-frequency data. By identifying the degree of the system that generated the data, we can build a surrogate model that, in addition to matching the data well (at low frequencies), has enhanced extrapolation capabilities (at high frequencies). We showcase the effectiveness and robustness of the newly proposed method through a suite of numerical tests.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/594e09448a7701a8dc362810a7d28f3f0e6378b6" target='_blank'>
-              Barycentric rational approximation for learning the index of a dynamical system from limited data
-              </a>
-            </td>
-          <td>
-            Davide Pradovera, I. V. Gosea, Jan Heiland
-          </td>
-          <td>2024-10-02</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>12</td>
-        </tr>
-
-        <tr id="Koopman spectral analysis plays a crucial role in understanding and modeling nonlinear dynamical systems as it reveals key system behaviors and long-term dynamics. However, the presence of measurement noise poses a significant challenge to accurately extracting spectral properties. In this work, we propose a robust method for identifying the Koopman operator and extracting its spectral characteristics in noisy environments. To address the impact of noise, our approach tackles an identification problem that accounts for both systematic errors from finite-dimensional approximations and measurement noise in the data. By incorporating Bayesian learning and Kalman smoothing, the method simultaneously identifies the Koopman operator and estimates system states, effectively decoupling these two error sources. The method's efficiency and robustness are demonstrated through extensive experiments, showcasing its accuracy across varying noise levels.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/cb268b2be065a16a965ef8982796602b22751276" target='_blank'>
-              Koopman Spectral Analysis from Noisy Measurements based on Bayesian Learning and Kalman Smoothing
-              </a>
-            </td>
-          <td>
-            Zhexuan Zeng, Jun Zhou, Yasen Wang, Zuowei Ping
-          </td>
-          <td>2024-10-01</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="Computationally efficient solutions for pseudometrics quantifying deviation from topological conjugacy between dynamical systems are presented. Deviation from conjugacy is quantified in a Pareto optimal sense that accounts for spectral properties of Koopman operators as well as trajectory geometry. Theoretical justification is provided for computing such pseudometrics in Koopman eigenfunction space rather than observable space. Furthermore, it is shown that theoretical consistency with topological conjugacy can be maintained when restricting the search for optimal transformations between systems to the unitary group. Therefore the pseudometrics are based on analytical solutions for unitary transformations in Koopman eigenfunction space. Geometric considerations for the deviation from conjugacy Pareto optimality problem are used to develop scalar pseudometrics that account for all possible optimal solutions given just two Pareto points. The approach is demonstrated on two example problems; the first being a simple benchmarking problem and the second an engineering example comparing the dynamics of morphological computation of biological nonlinear muscle actuators to simplified mad-made (including bioinspired) approaches. The benefits of considering operator and trajectory geometry based dissimilarity measures in a unified and consistent formalism is demonstrated. Overall, the deviation from conjugacy pseudometrics provide practical advantages in terms of efficiency and scalability, while maintaining theoretical consistency.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2d390a87c3f460a091e99066fd8147dcbeb83eaa" target='_blank'>
-              Efficient pseudometrics for data-driven comparisons of nonlinear dynamical systems
-              </a>
-            </td>
-          <td>
-            Bryan Glaz
-          </td>
-          <td>2024-09-27</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="Echo State Networks (ESNs) are recurrent neural networks usually employed for modeling nonlinear dynamic systems with relatively ease of training. By incorporating physical laws into the training of ESNs, Physics-Informed ESNs (PI-ESNs) were proposed initially to model chaotic dynamic systems without external inputs. They require less data for training since Ordinary Differential Equations (ODEs) of the considered system help to regularize the ESN. In this work, the PI-ESN is extended with external inputs to model controllable nonlinear dynamic systems. Additionally, an existing self-adaptive balancing loss method is employed to balance the contributions of the residual regression term and the physics-informed loss term in the total loss function. The experiments with two nonlinear systems modeled by ODEs, the Van der Pol oscillator and the four-tank system, and with one differential-algebraic (DAE) system, an electric submersible pump, revealed that the proposed PI-ESN outperforms the conventional ESN, especially in scenarios with limited data availability, showing that PI-ESNs can regularize an ESN model with external inputs previously trained on just a few datapoints, reducing its overfitting and improving its generalization error (up to 92% relative reduction in the test error). Further experiments demonstrated that the proposed PI-ESN is robust to parametric uncertainties in the ODE equations and that model predictive control using PI-ESN outperforms the one using plain ESN, particularly when training data is scarce.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e0b88bf05b77c55de080e84b7fc70b5d74109970" target='_blank'>
-              Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems
-              </a>
-            </td>
-          <td>
-            Eric Mochiutti, Eric A. Antonelo, Eduardo Camponogara
-          </td>
-          <td>2024-09-27</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>14</td>
-        </tr>
-
-        <tr id="Deep learning has achieved great success in modeling dynamical systems, providing data-driven simulators to predict complex phenomena, even without known governing equations. However, existing models have two major limitations: their narrow focus on mechanical systems and their tendency to treat systems as monolithic. These limitations reduce their applicability to dynamical systems in other domains, such as electrical and hydraulic systems, and to coupled systems. To address these limitations, we propose Poisson-Dirac Neural Networks (PoDiNNs), a novel framework based on the Dirac structure that unifies the port-Hamiltonian and Poisson formulations from geometric mechanics. This framework enables a unified representation of various dynamical systems across multiple domains as well as their interactions and degeneracies arising from couplings. Our experiments demonstrate that PoDiNNs offer improved accuracy and interpretability in modeling unknown coupled dynamical systems from data.">
+        <tr id="Efficiently estimating system dynamics from data is essential for minimizing data collection costs and improving model performance. This work addresses the challenge of designing future control inputs to maximize information gain, thereby improving the efficiency of the system identification process. We propose an approach that integrates informative input design into the Dynamic Mode Decomposition with control (DMDc) framework, which is well-suited for high-dimensional systems. By formulating an approximate convex optimization problem that minimizes the trace of the estimation error covariance matrix, we are able to efficiently reduce uncertainty in the model parameters while respecting constraints on the system states and control inputs. This method outperforms traditional techniques like Pseudo-Random Binary Sequences (PRBS) and orthogonal multisines, which do not adapt to the current system model and often gather redundant information. We validate our approach using aircraft and fluid dynamics simulations to demonstrate the practical applicability and effectiveness of our method. Our results show that strategically planning control inputs based on the current model enhances the accuracy of system identification while requiring less data. Furthermore, we provide our implementation and simulation interfaces as an open-source software package, facilitating further research development and use by industry practitioners.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/151acdc6b2f8dc3711777ee98c45ff7a1c2e7507" target='_blank'>
-              Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains
+          <td><a href="https://www.semanticscholar.org/paper/6127ef757da53faa534ca8a3e197c3eefe94e5c0" target='_blank'>
+              Informative Input Design for Dynamic Mode Decomposition
               </a>
             </td>
           <td>
-            Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, Takashi Matsubara
+            Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd
           </td>
-          <td>2024-10-15</td>
+          <td>2024-09-19</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>11</td>
-        </tr>
-
-        <tr id="The data-driven learning of solutions of partial differential equations can be based on a divide-and-conquer strategy. First, the high dimensional data is compressed to a latent space with an autoencoder; and, second, the temporal dynamics are inferred on the latent space with a form of recurrent neural network. In chaotic systems and turbulence, convolutional autoencoders and echo state networks (CAE-ESN) successfully forecast the dynamics, but little is known about whether the stability properties can also be inferred. We show that the CAE-ESN model infers the invariant stability properties and the geometry of the tangent space in the low-dimensional manifold (i.e. the latent space) through Lyapunov exponents and covariant Lyapunov vectors. This work opens up new opportunities for inferring the stability of high-dimensional chaotic systems in latent spaces.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/83f0122815199b700932f744a8335721b443c4d3" target='_blank'>
-              Inferring stability properties of chaotic systems on autoencoders' latent spaces
-              </a>
-            </td>
-          <td>
-            Elise Ozalp, L. Magri
-          </td>
-          <td>2024-10-23</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>2</td>
+          <td>17</td>
         </tr>
 
-        <tr id="Neural differential equations offer a powerful approach for learning dynamics from data. However, they do not impose known constraints that should be obeyed by the learned model. It is well-known that enforcing constraints in surrogate models can enhance their generalizability and numerical stability. In this paper, we introduce projected neural differential equations (PNDEs), a new method for constraining neural differential equations based on projection of the learned vector field to the tangent space of the constraint manifold. In tests on several challenging examples, including chaotic dynamical systems and state-of-the-art power grid models, PNDEs outperform existing methods while requiring fewer hyperparameters. The proposed approach demonstrates significant potential for enhancing the modeling of constrained dynamical systems, particularly in complex domains where accuracy and reliability are essential.">
+        <tr id="We consider the task of data-driven identification of dynamical systems, specifically for systems whose behavior at large frequencies is non-standard, as encoded by a non-trivial relative degree of the transfer function or, alternatively, a non-trivial index of a corresponding realization as a descriptor system. We develop novel surrogate modeling strategies that allow state-of-the-art rational approximation algorithms (e.g., AAA and vector fitting) to better handle data coming from such systems with non-trivial relative degree. Our contribution is twofold. On one hand, we describe a strategy to build rational surrogate models with prescribed relative degree, with the objective of mirroring the high-frequency behavior of the high-fidelity problem, when known. The surrogate model's desired degree is achieved through constraints on its barycentric coefficients, rather than through ad-hoc modifications of the rational form. On the other hand, we present a degree-identification routine that allows one to estimate the unknown relative degree of a system from low-frequency data. By identifying the degree of the system that generated the data, we can build a surrogate model that, in addition to matching the data well (at low frequencies), has enhanced extrapolation capabilities (at high frequencies). We showcase the effectiveness and robustness of the newly proposed method through a suite of numerical tests.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/17f2583b47e897b7f97f818a21bf3aa94c97c561" target='_blank'>
-              Projected Neural Differential Equations for Learning Constrained Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/594e09448a7701a8dc362810a7d28f3f0e6378b6" target='_blank'>
+              Barycentric rational approximation for learning the index of a dynamical system from limited data
               </a>
             </td>
           <td>
-            Alistair White, Anna Buttner, Maximilian Gelbrecht, Valentin Duruisseaux, Niki Kilbertus, Frank Hellmann, Niklas Boers
+            Davide Pradovera, I. V. Gosea, Jan Heiland
           </td>
-          <td>2024-10-31</td>
+          <td>2024-10-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>17</td>
+          <td>12</td>
         </tr>
 
-        <tr id="Extracting dynamic models from data is of enormous importance in understanding the properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of neural networks with a prescribed upper bound on their Lipschitz constant, to address the problem of data-efficient nonlinear system identification. Under the (fairly weak) assumption that the unknown system is Lipschitz continuous, we propose a method to estimate the approximation error bound of the trained network and the bound on the difference between the simulated trajectories by the trained models and the true system. Empirical results show that our method outperforms classic fully connected neural networks and Lipschitz regularized networks through simulation studies on three dynamical systems, and the advantage of our method is more noticeable when less data is used for training.">
+        <tr id="In this paper we use Gaussian processes (kernel methods) to learn mappings between trajectories of distinct differential equations. Our goal is to simplify both the representation and the solution of these equations. We begin by examining the Cole-Hopf transformation, a classical result that converts the nonlinear, viscous Burgers' equation into the linear heat equation. We demonstrate that this transformation can be effectively learned using Gaussian process regression, either from single or from multiple initial conditions of the Burgers equation. We then extend our methodology to discover mappings between initial conditions of a nonlinear partial differential equation (PDE) and a linear PDE, where the exact form of the linear PDE remains unknown and is inferred through Computational Graph Completion (CGC), a generalization of Gaussian Process Regression from approximating single input/output functions to approximating multiple input/output functions that interact within a computational graph. Further, we employ CGC to identify a local transformation from the nonlinear ordinary differential equation (ODE) of the Brusselator to its Poincar\'{e} normal form, capturing the dynamics around a Hopf bifurcation. We conclude by addressing the broader question of whether systematic transformations between nonlinear and linear PDEs can generally exist, suggesting avenues for future research.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e73a06b3980fa896f6481f7963514d872f3dc106" target='_blank'>
-              Data-Efficient System Identification via Lipschitz Neural Networks
+          <td><a href="https://www.semanticscholar.org/paper/bc191538175874d9f5a15a892a1c5f358da4ed5d" target='_blank'>
+              Gaussian Processes simplify differential equations
               </a>
             </td>
           <td>
-            Shiqing Wei, P. Krishnamurthy, F. Khorrami
+            Jonghyeon Lee, B. Hamzi, Y. Kevrekidis, H. Owhadi
           </td>
-          <td>2024-10-28</td>
+          <td>2024-10-03</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>40</td>
+          <td>36</td>
         </tr>
 
-        <tr id="In science, we are often interested in obtaining a generative model of the underlying system dynamics from observed time series. While powerful methods for dynamical systems reconstruction (DSR) exist when data come from a single domain, how to best integrate data from multiple dynamical regimes and leverage it for generalization is still an open question. This becomes particularly important when individual time series are short, and group-level information may help to fill in for gaps in single-domain data. At the same time, averaging is not an option in DSR, as it will wipe out crucial dynamical properties (e.g., limit cycles in one domain vs. chaos in another). Hence, a framework is needed that enables to efficiently harvest group-level (multi-domain) information while retaining all single-domain dynamical characteristics. Here we provide such a hierarchical approach and showcase it on popular DSR benchmarks, as well as on neuroscientific and medical time series. In addition to faithful reconstruction of all individual dynamical regimes, our unsupervised methodology discovers common low-dimensional feature spaces in which datasets with similar dynamics cluster. The features spanning these spaces were further dynamically highly interpretable, surprisingly in often linear relation to control parameters that govern the dynamics of the underlying system. Finally, we illustrate transfer learning and generalization to new parameter regimes.">
+        <tr id="The fluctuation-dissipation theorem is a cornerstone result in statistical mechanics that can be used to translate the statistics of the free natural variability of a system into information on its forced response to perturbations. By combining this viewpoint on response theory with the key ingredients of Koopmanism, it is possible to deconstruct virtually any response operator into a sum of terms, each associated with a specific mode of natural variability of the system. This dramatically improves the interpretability of the resulting response formulas. We show here on three simple yet mathematically meaningful examples how to use the Extended Dynamical Mode Decomposition (EDMD) algorithm on an individual trajectory of the system to compute with high accuracy correlation functions as well as Green functions associated with acting forcings. This demonstrates the great potential of using Koopman analysis for the key problem of evaluating and testing the sensitivity of a complex system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b48ae598cc4ac0fa1e02d37e094653205921b31c" target='_blank'>
-              Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
+          <td><a href="https://www.semanticscholar.org/paper/1084301b4c82602dbd6e472d6ae28af986788f13" target='_blank'>
+              Bridging the Gap between Koopmanism and Response Theory: Using Natural Variability to Predict Forced Response
               </a>
             </td>
           <td>
-            Manuel Brenner, Elias Weber, G. Koppe, Daniel Durstewitz
+            Niccolò Zagli, Matthew Colbrook, Valerio Lucarini, Igor Mezi'c, John Moroney
           </td>
-          <td>2024-10-07</td>
+          <td>2024-10-02</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>20</td>
+          <td>5</td>
         </tr>
 
-        <tr id="Fitting nonlinear dynamical models to sparse and noisy observations is fundamentally challenging. Identifying dynamics requires data assimilation (DA) to estimate system states, but DA requires an accurate dynamical model. To break this deadlock we present CODA, an end-to-end optimization scheme for jointly learning dynamics and DA directly from sparse and noisy observations. A neural network is trained to carry out data accurate, efficient and parallel-in-time DA, while free parameters of the dynamical system are simultaneously optimized. We carry out end-to-end learning directly on observation data, introducing a novel learning objective that combines unrolled auto-regressive dynamics with the data- and self-consistency terms of weak-constraint 4Dvar DA. By taking into account interactions between new and existing simulation components over multiple time steps, CODA can recover initial conditions, fit unknown dynamical parameters and learn neural network-based PDE terms to match both available observations and self-consistency constraints. In addition to facilitating end-to-end learning of dynamics and providing fast, amortized, non-sequential DA, CODA provides greater robustness to model misspecification than classical DA approaches.">
+        <tr id="Computationally efficient solutions for pseudometrics quantifying deviation from topological conjugacy between dynamical systems are presented. Deviation from conjugacy is quantified in a Pareto optimal sense that accounts for spectral properties of Koopman operators as well as trajectory geometry. Theoretical justification is provided for computing such pseudometrics in Koopman eigenfunction space rather than observable space. Furthermore, it is shown that theoretical consistency with topological conjugacy can be maintained when restricting the search for optimal transformations between systems to the unitary group. Therefore the pseudometrics are based on analytical solutions for unitary transformations in Koopman eigenfunction space. Geometric considerations for the deviation from conjugacy Pareto optimality problem are used to develop scalar pseudometrics that account for all possible optimal solutions given just two Pareto points. The approach is demonstrated on two example problems; the first being a simple benchmarking problem and the second an engineering example comparing the dynamics of morphological computation of biological nonlinear muscle actuators to simplified mad-made (including bioinspired) approaches. The benefits of considering operator and trajectory geometry based dissimilarity measures in a unified and consistent formalism is demonstrated. Overall, the deviation from conjugacy pseudometrics provide practical advantages in terms of efficiency and scalability, while maintaining theoretical consistency.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/29c4e83bece718bf215b80195b63bd422f419178" target='_blank'>
-              Combined Optimization of Dynamics and Assimilation with End-to-End Learning on Sparse Observations
+          <td><a href="https://www.semanticscholar.org/paper/2d390a87c3f460a091e99066fd8147dcbeb83eaa" target='_blank'>
+              Efficient pseudometrics for data-driven comparisons of nonlinear dynamical systems
               </a>
             </td>
           <td>
-            Vadim Zinchenko, David S. Greenberg
+            Bryan Glaz
           </td>
-          <td>2024-09-11</td>
+          <td>2024-09-27</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>0</td>
         </tr>
 
-        <tr id="Inferring dynamical models from data continues to be a significant challenge in computational biology, especially given the stochastic nature of many biological processes. We explore a common scenario in omics, where statistically independent cross-sectional samples are available at a few time points, and the goal is to infer the underlying diffusion process that generated the data. Existing inference approaches often simplify or ignore noise intrinsic to the system, compromising accuracy for the sake of optimization ease. We circumvent this compromise by inferring the phase-space probability flow that shares the same time-dependent marginal distributions as the underlying stochastic process. Our approach, probability flow inference (PFI), disentangles force from intrinsic stochasticity while retaining the algorithmic ease of ODE inference. Analytically, we prove that for Ornstein-Uhlenbeck processes the regularized PFI formalism yields a unique solution in the limit of well-sampled distributions. In practical applications, we show that PFI enables accurate parameter and force estimation in high-dimensional stochastic reaction networks, and that it allows inference of cell differentiation dynamics with molecular noise, outperforming state-of-the-art approaches.">
+        <tr id="Koopman spectral analysis plays a crucial role in understanding and modeling nonlinear dynamical systems as it reveals key system behaviors and long-term dynamics. However, the presence of measurement noise poses a significant challenge to accurately extracting spectral properties. In this work, we propose a robust method for identifying the Koopman operator and extracting its spectral characteristics in noisy environments. To address the impact of noise, our approach tackles an identification problem that accounts for both systematic errors from finite-dimensional approximations and measurement noise in the data. By incorporating Bayesian learning and Kalman smoothing, the method simultaneously identifies the Koopman operator and estimates system states, effectively decoupling these two error sources. The method's efficiency and robustness are demonstrated through extensive experiments, showcasing its accuracy across varying noise levels.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0ee317723f9d9f3110f643abedc4ab2f39829cff" target='_blank'>
-              Inferring biological processes with intrinsic noise from cross-sectional data
+          <td><a href="https://www.semanticscholar.org/paper/cb268b2be065a16a965ef8982796602b22751276" target='_blank'>
+              Koopman Spectral Analysis from Noisy Measurements based on Bayesian Learning and Kalman Smoothing
               </a>
             </td>
           <td>
-            S. Maddu, Victor Chardès, Michael. J. Shelley
+            Zhexuan Zeng, Jun Zhou, Yasen Wang, Zuowei Ping
           </td>
-          <td>2024-10-10</td>
+          <td>2024-10-01</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
-        </tr>
-
-        <tr id="Complex networks describe important structures in nature and society, composed of nodes and the edges that connect them. The evolution of these networks is typically described by dynamics, which are labor-intensive and require expert knowledge to derive. However, because the complex network involves noisy observations from multiple trajectories of nodes, existing symbolic regression methods are either not applicable or ineffective on its dynamics. In this paper, we propose Physically Inspired Neural Dynamics Symbolic Regression (PI-NDSR), a method based on neural networks and genetic programming to automatically learn the symbolic expression of dynamics. Our method consists of two key components: a Physically Inspired Neural Dynamics (PIND) to augment and denoise trajectories through observed trajectory interpolation; and a coordinated genetic search algorithm to derive symbolic expressions. This algorithm leverages references of node dynamics and edge dynamics from neural dynamics to avoid overfitted expressions in symbolic space. We evaluate our method on synthetic datasets generated by various dynamics and real datasets on disease spreading. The results demonstrate that PI-NDSR outperforms the existing method in terms of both recovery probability and error.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/96ac5784a6f229c10124256b1a88b4ac9ded208d" target='_blank'>
-              Neural Symbolic Regression of Complex Network Dynamics
-              </a>
-            </td>
-          <td>
-            Haiquan Qiu, Shuzhi Liu, Quanming Yao
-          </td>
-          <td>2024-10-15</td>
-          <td>ArXiv</td>
           <td>0</td>
-          <td>1</td>
         </tr>
 
-        <tr id="Even though a variety of methods have been proposed in the literature, efficient and effective latent-space control (i.e., control in a learned low-dimensional space) of physical systems remains an open challenge. We argue that a promising avenue is to leverage powerful and well-understood closed-form strategies from control theory literature in combination with learned dynamics, such as potential-energy shaping. We identify three fundamental shortcomings in existing latent-space models that have so far prevented this powerful combination: (i) they lack the mathematical structure of a physical system, (ii) they do not inherently conserve the stability properties of the real systems, (iii) these methods do not have an invertible mapping between input and latent-space forcing. This work proposes a novel Coupled Oscillator Network (CON) model that simultaneously tackles all these issues. More specifically, (i) we show analytically that CON is a Lagrangian system - i.e., it possesses well-defined potential and kinetic energy terms. Then, (ii) we provide formal proof of global Input-to-State stability using Lyapunov arguments. Moving to the experimental side, we demonstrate that CON reaches SoA performance when learning complex nonlinear dynamics of mechanical systems directly from images. An additional methodological innovation contributing to achieving this third goal is an approximated closed-form solution for efficient integration of network dynamics, which eases efficient training. We tackle (iii) by approximating the forcing-to-input mapping with a decoder that is trained to reconstruct the input based on the encoded latent space force. Finally, we show how these properties enable latent-space control. We use an integral-saturated PID with potential force compensation and demonstrate high-quality performance on a soft robot using raw pixels as the only feedback information.">
+        <tr id="Echo State Networks (ESNs) are recurrent neural networks usually employed for modeling nonlinear dynamic systems with relatively ease of training. By incorporating physical laws into the training of ESNs, Physics-Informed ESNs (PI-ESNs) were proposed initially to model chaotic dynamic systems without external inputs. They require less data for training since Ordinary Differential Equations (ODEs) of the considered system help to regularize the ESN. In this work, the PI-ESN is extended with external inputs to model controllable nonlinear dynamic systems. Additionally, an existing self-adaptive balancing loss method is employed to balance the contributions of the residual regression term and the physics-informed loss term in the total loss function. The experiments with two nonlinear systems modeled by ODEs, the Van der Pol oscillator and the four-tank system, and with one differential-algebraic (DAE) system, an electric submersible pump, revealed that the proposed PI-ESN outperforms the conventional ESN, especially in scenarios with limited data availability, showing that PI-ESNs can regularize an ESN model with external inputs previously trained on just a few datapoints, reducing its overfitting and improving its generalization error (up to 92% relative reduction in the test error). Further experiments demonstrated that the proposed PI-ESN is robust to parametric uncertainties in the ODE equations and that model predictive control using PI-ESN outperforms the one using plain ESN, particularly when training data is scarce.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f98776cb5572a95e9eb51860bf2285d6222c4acd" target='_blank'>
-              Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space
+          <td><a href="https://www.semanticscholar.org/paper/e0b88bf05b77c55de080e84b7fc70b5d74109970" target='_blank'>
+              Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems
               </a>
             </td>
           <td>
-            Maximilian Stölzle, C. D. Santina
+            Eric Mochiutti, Eric A. Antonelo, Eduardo Camponogara
           </td>
-          <td>2024-09-13</td>
+          <td>2024-09-27</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>19</td>
+          <td>14</td>
         </tr>
 
-        <tr id="Data-driven approaches are increasingly popular for identifying dynamical systems due to improved accuracy and availability of sensor data. However, relying solely on data for identification does not guarantee that the identified systems will maintain their physical properties or that the predicted models will generalize well. In this paper, we propose a novel method for system identification by integrating a neural network as the first-order derivative of a Taylor series expansion instead of learning a dynamical function directly. This approach, called Monotonic Taylor Neural Networks (MTNN), aims to ensure monotonic properties of dynamical systems by constraining the conditions for the output of the neural networks model to be either always non-positive or non-negative. These conditions are constructed in two ways: by designing a new neural network architecture or by regularizing the loss function for training. The proposed method demonstrates better performance compared to methods without constraints on the monotonic properties of the systems when tested with experimental data from two real-world systems, including HVAC and TCLab. Furthermore, MTNN shows good performance in an actual control application when using a model predictive controller for a nonlinear MIMO system, illustrating the practical applications of this method.">
+        <tr id="Physics-Informed Neural Networks (PINNs) are used to study vortex solutions in the 2+1 dimensional nonlinear partial differential equations. These solutions include the regularized long-wave (RLW) equation and the Zakharov-Kuznetsov (ZK) equation, which are toy models of the geostrophic shallow water model in the planetary atmosphere. PINNs successfully solve these equations in the forward process and the solutions are obtained using the mesh-free approach and automatic differentiation while accounting for conservation laws. In the inverse process, the proper equations can be successfully derived from a given training data. Since these equations have a lot in common, there are situations when substantial misidentification arises during the inverse analysis. We consider PINNs with conservation laws (referred to as cPINNs), deformations of the initial profiles, and a friction approach that provides excellent discrimination of the equations to improve the identification's resolution.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4d2c43a9d06a52e3c29ff5fcdd511820730f95a6" target='_blank'>
-              Physics-Constrained Taylor Neural Networks for Learning and Control of Dynamical Systems
+          <td><a href="https://www.semanticscholar.org/paper/09235f2156a7d20de3910de1509ae6d574bde0a5" target='_blank'>
+              Discovery of Quasi-Integrable Equations from traveling-wave data using the Physics-Informed Neural Networks
               </a>
             </td>
           <td>
-            Nam T. Nguyen, Juan C. Tique
+            A. Nakamula, N. Sawado, K. Shimasaki, Y. Shimazaki, Y. Suzuki, K. Toda
           </td>
-          <td>2024-10-03</td>
+          <td>2024-10-23</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>1</td>
         </tr>
 
-        <tr id="Macroscopic observables of a system are of keen interest in real applications such as the design of novel materials. Current methods rely on microscopic trajectory simulations, where the forces on all microscopic coordinates need to be computed or measured. However, this can be computationally prohibitive for realistic systems. In this paper, we propose a method to learn macroscopic dynamics requiring only force computations on a subset of the microscopic coordinates. Our method relies on a sparsity assumption: the force on each microscopic coordinate relies only on a small number of other coordinates. The main idea of our approach is to map the training procedure on the macroscopic coordinates back to the microscopic coordinates, on which partial force computations can be used as stochastic estimation to update model parameters. We provide a theoretical justification of this under suitable conditions. We demonstrate the accuracy, force computation efficiency, and robustness of our method on learning macroscopic closure models from a variety of microscopic systems, including those modeled by partial differential equations or molecular dynamics simulations.">
+        <tr id="Deep learning has achieved great success in modeling dynamical systems, providing data-driven simulators to predict complex phenomena, even without known governing equations. However, existing models have two major limitations: their narrow focus on mechanical systems and their tendency to treat systems as monolithic. These limitations reduce their applicability to dynamical systems in other domains, such as electrical and hydraulic systems, and to coupled systems. To address these limitations, we propose Poisson-Dirac Neural Networks (PoDiNNs), a novel framework based on the Dirac structure that unifies the port-Hamiltonian and Poisson formulations from geometric mechanics. This framework enables a unified representation of various dynamical systems across multiple domains as well as their interactions and degeneracies arising from couplings. Our experiments demonstrate that PoDiNNs offer improved accuracy and interpretability in modeling unknown coupled dynamical systems from data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5a0024df30be5aef19f718a676bcb4da2057cf80" target='_blank'>
-              Learning Macroscopic Dynamics from Partial Microscopic Observations
+          <td><a href="https://www.semanticscholar.org/paper/151acdc6b2f8dc3711777ee98c45ff7a1c2e7507" target='_blank'>
+              Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains
               </a>
             </td>
           <td>
-            Mengyi Chen, Qianxiao Li
+            Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, Takashi Matsubara
           </td>
-          <td>2024-10-31</td>
+          <td>2024-10-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>11</td>
         </tr>
 
     </tbody>
@@ -1570,7 +1570,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Symbolic regression</i>
 
   var trace1 = {
     x: ['2024'],
-    y: [2],
+    y: [3],
     name: 'Num of citations',
     yaxis: 'y1',
     type: 'scatter'
diff --git a/site/Time-series forecasting/index.html b/site/Time-series forecasting/index.html
index 4c8e551a..589127a8 100644
--- a/site/Time-series forecasting/index.html	
+++ b/site/Time-series forecasting/index.html	
@@ -453,7 +453,7 @@ <h1>Time-series forecasting</h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:59 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:40 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -517,8 +517,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan
           </td>
           <td>2023-07-07</td>
-          <td>IEEE transactions on pattern analysis and machine intelligence</td>
-          <td>80</td>
+          <td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td>
+          <td>82</td>
           <td>50</td>
 
             <td><a href='../recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9' target='_blank'>
@@ -538,7 +538,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2021-10-11</td>
           <td>ArXiv, International Conference on Learning Representations</td>
-          <td>84</td>
+          <td>86</td>
           <td>47</td>
 
             <td><a href='../recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba' target='_blank'>
@@ -558,7 +558,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2023-02-08</td>
           <td>ArXiv, Neural Information Processing Systems</td>
-          <td>21</td>
+          <td>22</td>
           <td>50</td>
 
             <td><a href='../recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419' target='_blank'>
@@ -577,7 +577,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Andrea Cini, Daniele Zambon, C. Alippi
           </td>
           <td>2022-05-26</td>
-          <td>Journal of machine learning research, J. Mach. Learn. Res.</td>
+          <td>J. Mach. Learn. Res., Journal of machine learning research</td>
           <td>14</td>
           <td>50</td>
 
@@ -598,7 +598,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2023-10-24</td>
           <td>ArXiv, arXiv.org</td>
-          <td>8</td>
+          <td>9</td>
           <td>50</td>
 
             <td><a href='../recommendations/ccea298edb788edf821aef58f0952c3e8debc25a' target='_blank'>
@@ -618,7 +618,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2023-10-11</td>
           <td>ArXiv, Neural Information Processing Systems</td>
-          <td>190</td>
+          <td>198</td>
           <td>14</td>
 
             <td><a href='../recommendations/123acfbccca0460171b6b06a4012dbb991cde55b' target='_blank'>
@@ -638,7 +638,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2024-02-01</td>
           <td>ArXiv, arXiv.org</td>
-          <td>55</td>
+          <td>56</td>
           <td>5</td>
 
             <td><a href='../recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f' target='_blank'>
@@ -657,8 +657,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou
           </td>
           <td>2023-10-14</td>
-          <td>International Conference on Machine Learning, ArXiv</td>
-          <td>95</td>
+          <td>ArXiv, International Conference on Machine Learning</td>
+          <td>103</td>
           <td>14</td>
 
             <td><a href='../recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f' target='_blank'>
@@ -677,8 +677,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo
           </td>
           <td>2024-02-04</td>
-          <td>International Conference on Machine Learning, ArXiv</td>
-          <td>63</td>
+          <td>ArXiv, International Conference on Machine Learning</td>
+          <td>71</td>
           <td>25</td>
 
             <td><a href='../recommendations/4a111f7a3b56d0468f13104999844885157ef17d' target='_blank'>
@@ -698,7 +698,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2023-10-03</td>
           <td>ArXiv, International Conference on Learning Representations</td>
-          <td>184</td>
+          <td>195</td>
           <td>9</td>
 
             <td><a href='../recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277' target='_blank'>
@@ -707,7 +707,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
 
         </tr>
 
-        <tr id="Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characteristics. Consequently, recent research efforts have focused on developing pre-trained TS forecasting models. These models, whether built from scratch or adapted from large language models (LLMs), excel in zero/few-shot forecasting tasks. However, they are limited by slow performance, high computational demands, and neglect of cross-channel and exogenous correlations. To address this, we introduce Tiny Time Mixers (TTM), a compact model (starting from 1M parameters) with effective transfer learning capabilities, trained exclusively on public TS datasets. TTM, based on the light-weight TSMixer architecture, incorporates innovations like adaptive patching, diverse resolution sampling, and resolution prefix tuning to handle pre-training on varied dataset resolutions with minimal model capacity. Additionally, it employs multi-level modeling to capture channel correlations and infuse exogenous signals during fine-tuning. TTM outperforms existing popular benchmarks in zero/few-shot forecasting by (4-40\%), while reducing computational requirements significantly. Moreover, TTMs are lightweight and can be executed even on CPU-only machines, enhancing usability and fostering wider adoption in resource-constrained environments. Model weights for our initial variant (TTM-Q) are available at https://huggingface.co/ibm-granite/granite-timeseries-ttm-v1. Model weights for more sophisticated variants (TTM-B, TTM-E, and TTM-A) will be shared soon. The source code for TTM can be accessed at https://github.com/ibm-granite/granite-tsfm/tree/main/tsfm_public/models/tinytimemixer.">
+        <tr id="Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characteristics. Consequently, recent research efforts have focused on developing pre-trained TS forecasting models. These models, whether built from scratch or adapted from large language models (LLMs), excel in zero/few-shot forecasting tasks. However, they are limited by slow performance, high computational demands, and neglect of cross-channel and exogenous correlations. To address this, we introduce Tiny Time Mixers (TTM), a compact model (starting from 1M parameters) with effective transfer learning capabilities, trained exclusively on public TS datasets. TTM, based on the light-weight TSMixer architecture, incorporates innovations like adaptive patching, diverse resolution sampling, and resolution prefix tuning to handle pre-training on varied dataset resolutions with minimal model capacity. Additionally, it employs multi-level modeling to capture channel correlations and infuse exogenous signals during fine-tuning. TTM outperforms existing popular benchmarks in zero/few-shot forecasting by (4-40%), while reducing computational requirements significantly. Moreover, TTMs are lightweight and can be executed even on CPU-only machines, enhancing usability and fostering wider adoption in resource-constrained environments. The model weights for reproducibility and research use are available at https://huggingface.co/ibm/ttm-research-r2/, while enterprise-use weights under the Apache license can be accessed as follows: the initial TTM-Q variant at https://huggingface.co/ibm-granite/granite-timeseries-ttm-r1, and the latest variants (TTM-B, TTM-E, TTM-A) weights are available at https://huggingface.co/ibm-granite/granite-timeseries-ttm-r2.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6" target='_blank'>
                 Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
@@ -738,7 +738,7 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
           </td>
           <td>2022-06-17</td>
           <td>ArXiv, Neural Information Processing Systems</td>
-          <td>209</td>
+          <td>211</td>
           <td>47</td>
 
             <td><a href='../recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37' target='_blank'>
@@ -757,8 +757,8 @@ <h3 id="manually_curated_articles">Manually curated articles on <i>Time-series f
             Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, M. Zitnik
           </td>
           <td>2023-02-06</td>
-          <td>DBLP, ArXiv</td>
-          <td>37</td>
+          <td>ArXiv, DBLP</td>
+          <td>38</td>
           <td>47</td>
 
             <td><a href='../recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14' target='_blank'>
@@ -862,6 +862,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
     </thead>
     <tbody>
 
+        <tr id="Time series foundation models have demonstrated strong performance in zero-shot learning, making them well-suited for predicting rapidly evolving patterns in real-world applications where relevant training data are scarce. However, most of these models rely on the Transformer architecture, which incurs quadratic complexity as input length increases. To address this, we introduce TSMamba, a linear-complexity foundation model for time series forecasting built on the Mamba architecture. The model captures temporal dependencies through both forward and backward Mamba encoders, achieving high prediction accuracy. To reduce reliance on large datasets and lower training costs, TSMamba employs a two-stage transfer learning process that leverages pretrained Mamba LLMs, allowing effective time series modeling with a moderate training set. In the first stage, the forward and backward backbones are optimized via patch-wise autoregressive prediction; in the second stage, the model trains a prediction head and refines other components for long-term forecasting. While the backbone assumes channel independence to manage varying channel numbers across datasets, a channel-wise compressed attention module is introduced to capture cross-channel dependencies during fine-tuning on specific multivariate datasets. Experiments show that TSMamba's zero-shot performance is comparable to state-of-the-art time series foundation models, despite using significantly less training data. It also achieves competitive or superior full-shot performance compared to task-specific prediction models. The code will be made publicly available.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/30abc3fad3c88886c189e216c8414b18c396c6bd" target='_blank'>
+              A Mamba Foundation Model for Time Series Forecasting
+              </a>
+            </td>
+          <td>
+            Haoyu Ma, Yushu Chen, Wenlai Zhao, Jinzhe Yang, Yingsheng Ji, Xinghua Xu, Xiaozhu Liu, Hao Jing, Shengzhuo Liu, Guangwen Yang
+          </td>
+          <td>2024-11-05</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>7</td>
+        </tr>
+
         <tr id="Time series forecasting is prevalent in extensive real-world applications, such as financial analysis and energy planning. Previous studies primarily focus on time series modality, endeavoring to capture the intricate variations and dependencies inherent in time series. Beyond numerical time series data, we notice that metadata (e.g.~dataset and variate descriptions) also carries valuable information essential for forecasting, which can be used to identify the application scenario and provide more interpretable knowledge than digit sequences. Inspired by this observation, we propose a Metadata-informed Time Series Transformer (MetaTST), which incorporates multiple levels of context-specific metadata into Transformer forecasting models to enable informative time series forecasting. To tackle the unstructured nature of metadata, MetaTST formalizes them into natural languages by pre-designed templates and leverages large language models (LLMs) to encode these texts into metadata tokens as a supplement to classic series tokens, resulting in an informative embedding. Further, a Transformer encoder is employed to communicate series and metadata tokens, which can extend series representations by metadata information for more accurate forecasting. This design also allows the model to adaptively learn context-specific patterns across various scenarios, which is particularly effective in handling large-scale, diverse-scenario forecasting tasks. Experimentally, MetaTST achieves state-of-the-art compared to advanced time series models and LLM-based methods on widely acknowledged short- and long-term forecasting benchmarks, covering both single-dataset individual and multi-dataset joint training settings.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/f46d685e05e6742f73946ce70533672b02e5f1ed" target='_blank'>
@@ -903,7 +918,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           </td>
           <td>2024-09-24</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>9</td>
         </tr>
 
@@ -952,19 +967,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>12</td>
         </tr>
 
-        <tr id="Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and univariate datasets demonstrate that LLM-Mixer achieves competitive performance, outperforming recent state-of-the-art models across various forecasting horizons. This work highlights the potential of combining multiscale analysis and LLMs for effective and scalable time-series forecasting.">
+        <tr id="Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-series into the future. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks compared to supervised deep learning methods, statistical models, as well as other time-series foundation models. Interestingly, our in-context fine-tuning approach even rivals the performance of a foundation model that is explicitly fine-tuned on the target domain.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/867d69684fa5692cfb29cb0f6967415f7a9ae7bd" target='_blank'>
-              LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/1216a29c0beb36a2b621b59d36877edf05afcf8c" target='_blank'>
+              In-Context Fine-Tuning for Time-Series Foundation Models
               </a>
             </td>
           <td>
-            Md. Kowsher, Md. Shohanur, Islam Sobuj, Nusrat Jahan Prottasha, E. A. Alanis, O. Garibay, Niloofar Yousefi, Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Xuan Zhang, Xun Liang, Aakas Zhiyuli, Shusen Zhang, Rui Xu, Bo Wu. 2019, Huaqin Zhao, Zheng Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Gengchen Mai, Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wan Zhang
+            Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou
           </td>
-          <td>2024-10-15</td>
+          <td>2024-10-31</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>34</td>
+          <td>14</td>
         </tr>
 
         <tr id="Time series foundation models have shown impressive performance on a variety of tasks, across a wide range of domains, even in zero-shot settings. However, most of these models are designed to handle short univariate time series as an input. This limits their practical use, especially in domains such as healthcare with copious amounts of long and multivariate data with strong temporal and intra-variate dependencies. Our study bridges this gap by cataloging and systematically comparing various context expansion techniques from both language and time series domains, and introducing a novel compressive memory mechanism to allow encoder-only TSFMs to effectively model intra-variate dependencies. We demonstrate the benefits of our approach by imbuing MOMENT, a recent family of multi-task time series foundation models, with the multivariate context.">
@@ -997,19 +1012,19 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>25</td>
         </tr>
 
-        <tr id="In processing multiple time series, accounting for the individual features of each sequence can be challenging. To address this, modern deep learning methods for time series analysis combine a shared (global) model with local layers, specific to each time series, often implemented as learnable embeddings. Ideally, these local embeddings should encode meaningful representations of the unique dynamics of each sequence. However, when these are learned end-to-end as parameters of a forecasting model, they may end up acting as mere sequence identifiers. Shared processing blocks may then become reliant on such identifiers, limiting their transferability to new contexts. In this paper, we address this issue by investigating methods to regularize the learning of local learnable embeddings for time series processing. Specifically, we perform the first extensive empirical study on the subject and show how such regularizations consistently improve performance in widely adopted architectures. Furthermore, we show that methods preventing the co-adaptation of local and global parameters are particularly effective in this context. This hypothesis is validated by comparing several methods preventing the downstream models from relying on sequence identifiers, going as far as completely resetting the embeddings during training. The obtained results provide an important contribution to understanding the interplay between learnable local parameters and shared processing layers: a key challenge in modern time series processing models and a step toward developing effective foundation models for time series.">
+        <tr id="Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and univariate datasets demonstrate that LLM-Mixer achieves competitive performance, outperforming recent state-of-the-art models across various forecasting horizons. This work highlights the potential of combining multiscale analysis and LLMs for effective and scalable time-series forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/58186a3ffbc117d00588f46184acb7ae78c2bd52" target='_blank'>
-              On the Regularization of Learnable Embeddings for Time Series Processing
+          <td><a href="https://www.semanticscholar.org/paper/867d69684fa5692cfb29cb0f6967415f7a9ae7bd" target='_blank'>
+              LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting
               </a>
             </td>
           <td>
-            L. Butera, G. Felice, Andrea Cini, C. Alippi
+            Md. Kowsher, Md. Shohanur, Islam Sobuj, Nusrat Jahan Prottasha, E. A. Alanis, O. Garibay, Niloofar Yousefi, Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Xuan Zhang, Xun Liang, Aakas Zhiyuli, Shusen Zhang, Rui Xu, Bo Wu. 2019, Huaqin Zhao, Zheng Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Gengchen Mai, Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wan Zhang
           </td>
-          <td>2024-10-18</td>
+          <td>2024-10-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>50</td>
+          <td>34</td>
         </tr>
 
         <tr id="Self-supervised learning has become a popular and effective approach for enhancing time series forecasting, enabling models to learn universal representations from unlabeled data. However, effectively capturing both the global sequence dependence and local detail features within time series data remains challenging. To address this, we propose a novel generative self-supervised method called TimeDART, denoting Diffusion Auto-regressive Transformer for Time series forecasting. In TimeDART, we treat time series patches as basic modeling units. Specifically, we employ an self-attention based Transformer encoder to model the dependencies of inter-patches. Additionally, we introduce diffusion and denoising mechanisms to capture the detail locality features of intra-patch. Notably, we design a cross-attention-based denoising decoder that allows for adjustable optimization difficulty in the self-supervised task, facilitating more effective self-supervised pre-training. Furthermore, the entire model is optimized in an auto-regressive manner to obtain transferable representations. Extensive experiments demonstrate that TimeDART achieves state-of-the-art fine-tuning performance compared to the most advanced competitive methods in forecasting tasks. Our code is publicly available at https://github.com/Melmaphother/TimeDART.">
@@ -1027,34 +1042,49 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>11</td>
         </tr>
 
-        <tr id="Large Language Models have demonstrated impressive performance in many pivotal web applications such as sensor data analysis. However, since LLMs are not designed for time series tasks, simpler models like linear regressions can often achieve comparable performance with far less complexity. In this study, we perform extensive experiments to assess the effectiveness of applying LLMs to key time series tasks, including forecasting, classification, imputation, and anomaly detection. We compare the performance of LLMs against simpler baseline models, such as single-layer linear models and randomly initialized LLMs. Our results reveal that LLMs offer minimal advantages for these core time series tasks and may even distort the temporal structure of the data. In contrast, simpler models consistently outperform LLMs while requiring far fewer parameters. Furthermore, we analyze existing reprogramming techniques and show, through data manifold analysis, that these methods fail to effectively align time series data with language and display pseudo-alignment behaviour in embedding space. Our findings suggest that the performance of LLM-based methods in time series tasks arises from the intrinsic characteristics and structure of time series data, rather than any meaningful alignment with the language model architecture.">
+        <tr id="In processing multiple time series, accounting for the individual features of each sequence can be challenging. To address this, modern deep learning methods for time series analysis combine a shared (global) model with local layers, specific to each time series, often implemented as learnable embeddings. Ideally, these local embeddings should encode meaningful representations of the unique dynamics of each sequence. However, when these are learned end-to-end as parameters of a forecasting model, they may end up acting as mere sequence identifiers. Shared processing blocks may then become reliant on such identifiers, limiting their transferability to new contexts. In this paper, we address this issue by investigating methods to regularize the learning of local learnable embeddings for time series processing. Specifically, we perform the first extensive empirical study on the subject and show how such regularizations consistently improve performance in widely adopted architectures. Furthermore, we show that methods preventing the co-adaptation of local and global parameters are particularly effective in this context. This hypothesis is validated by comparing several methods preventing the downstream models from relying on sequence identifiers, going as far as completely resetting the embeddings during training. The obtained results provide an important contribution to understanding the interplay between learnable local parameters and shared processing layers: a key challenge in modern time series processing models and a step toward developing effective foundation models for time series.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5d1758cc07c6914d280b5690b91d04784c15d7d3" target='_blank'>
-              Revisited Large Language Model for Time Series Analysis through Modality Alignment
+          <td><a href="https://www.semanticscholar.org/paper/58186a3ffbc117d00588f46184acb7ae78c2bd52" target='_blank'>
+              On the Regularization of Learnable Embeddings for Time Series Processing
               </a>
             </td>
           <td>
-            L. Zheng, C. Dong, W. Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
+            L. Butera, G. Felice, Andrea Cini, C. Alippi
           </td>
-          <td>2024-10-16</td>
+          <td>2024-10-18</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>7</td>
+          <td>50</td>
         </tr>
 
-        <tr id="Irregularly sampled time series forecasting, characterized by non-uniform intervals, is prevalent in practical applications. However, previous research have been focused on regular time series forecasting, typically relying on transformer architectures. To extend transformers to handle irregular time series, we tackle the positional embedding which represents the temporal information of the data. We propose CTLPE, a method learning a continuous linear function for encoding temporal information. The two challenges of irregular time series, inconsistent observation patterns and irregular time gaps, are solved by learning a continuous-time function and concise representation of position. Additionally, the linear continuous function is empirically shown superior to other continuous functions by learning a neural controlled differential equation-based positional embedding, and theoretically supported with properties of ideal positional embedding. CTLPE outperforms existing techniques across various irregularly-sampled time series datasets, showcasing its enhanced efficacy.">
+        <tr id="Time series forecasting remains a critical challenge across various domains, often complicated by high-dimensional data and long-term dependencies. This paper presents a novel transformer architecture for time series forecasting, incorporating two key innovations: parameter sharing (PS) and Spatial-Temporal Segment Attention (SegAtt). We also define the time series segment as the concatenation of sequence patches from the same positions across different variables. The proposed model, PSformer, reduces the number of training parameters through the parameter sharing mechanism, thereby improving model efficiency and scalability. The introduction of SegAtt could enhance the capability of capturing local spatio-temporal dependencies by computing attention over the segments, and improve global representation by integrating information across segments. The combination of parameter sharing and SegAtt significantly improves the forecasting performance. Extensive experiments on benchmark datasets demonstrate that PSformer outperforms popular baselines and other transformer-based approaches in terms of accuracy and scalability, establishing itself as an accurate and scalable tool for time series forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3def10f5aec43ee78891e66d0efb31ad4f06d5b0" target='_blank'>
-              Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/496f3b7be63dc5765051593b2fe1ffd19782e3ea" target='_blank'>
+              PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting
               </a>
             </td>
           <td>
-            Byunghyun Kim, Jae-Gil Lee
+            Yanlong Wang, Jian Xu, Fei Ma, Shao-Lun Huang, Danny D. Sun, Xiao-Ping Zhang
           </td>
-          <td>2024-09-30</td>
+          <td>2024-11-03</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>1</td>
+        </tr>
+
+        <tr id="Large Language Models have demonstrated impressive performance in many pivotal web applications such as sensor data analysis. However, since LLMs are not designed for time series tasks, simpler models like linear regressions can often achieve comparable performance with far less complexity. In this study, we perform extensive experiments to assess the effectiveness of applying LLMs to key time series tasks, including forecasting, classification, imputation, and anomaly detection. We compare the performance of LLMs against simpler baseline models, such as single-layer linear models and randomly initialized LLMs. Our results reveal that LLMs offer minimal advantages for these core time series tasks and may even distort the temporal structure of the data. In contrast, simpler models consistently outperform LLMs while requiring far fewer parameters. Furthermore, we analyze existing reprogramming techniques and show, through data manifold analysis, that these methods fail to effectively align time series data with language and display pseudo-alignment behaviour in embedding space. Our findings suggest that the performance of LLM-based methods in time series tasks arises from the intrinsic characteristics and structure of time series data, rather than any meaningful alignment with the language model architecture.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/5d1758cc07c6914d280b5690b91d04784c15d7d3" target='_blank'>
+              Revisited Large Language Model for Time Series Analysis through Modality Alignment
+              </a>
+            </td>
+          <td>
+            L. Zheng, C. Dong, W. Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
+          </td>
+          <td>2024-10-16</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>7</td>
         </tr>
 
         <tr id="Time-series forecasting has seen significant advancements with the introduction of token prediction mechanisms such as multi-head attention. However, these methods often struggle to achieve the same performance as in language modeling, primarily due to the quadratic computational cost and the complexity of capturing long-range dependencies in time-series data. State-space models (SSMs), such as Mamba, have shown promise in addressing these challenges by offering efficient solutions with linear RNNs capable of modeling long sequences with larger context windows. However, there remains room for improvement in accuracy and scalability. We propose the use of Test-Time Training (TTT) modules in a parallel architecture to enhance performance in long-term time series forecasting. Through extensive experiments on standard benchmark datasets, we demonstrate that TTT modules consistently outperform state-of-the-art models, including the Mamba-based TimeMachine, particularly in scenarios involving extended sequence and prediction lengths. Our results show significant improvements in Mean Squared Error (MSE) and Mean Absolute Error (MAE), especially on larger datasets such as Electricity, Traffic, and Weather, underscoring the effectiveness of TTT in capturing long-range dependencies. Additionally, we explore various convolutional architectures within the TTT framework, showing that even simple configurations like 1D convolution with small filters can achieve competitive results. This work sets a new benchmark for time-series forecasting and lays the groundwork for future research in scalable, high-performance forecasting models.">
@@ -1072,6 +1102,21 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>4</td>
         </tr>
 
+        <tr id="Irregularly sampled time series forecasting, characterized by non-uniform intervals, is prevalent in practical applications. However, previous research have been focused on regular time series forecasting, typically relying on transformer architectures. To extend transformers to handle irregular time series, we tackle the positional embedding which represents the temporal information of the data. We propose CTLPE, a method learning a continuous linear function for encoding temporal information. The two challenges of irregular time series, inconsistent observation patterns and irregular time gaps, are solved by learning a continuous-time function and concise representation of position. Additionally, the linear continuous function is empirically shown superior to other continuous functions by learning a neural controlled differential equation-based positional embedding, and theoretically supported with properties of ideal positional embedding. CTLPE outperforms existing techniques across various irregularly-sampled time series datasets, showcasing its enhanced efficacy.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/3def10f5aec43ee78891e66d0efb31ad4f06d5b0" target='_blank'>
+              Continuous-Time Linear Positional Embedding for Irregular Time Series Forecasting
+              </a>
+            </td>
+          <td>
+            Byunghyun Kim, Jae-Gil Lee
+          </td>
+          <td>2024-09-30</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>2</td>
+        </tr>
+
         <tr id="Attention-based architectures have become ubiquitous in time series forecasting tasks, including spatio-temporal (STF) and long-term time series forecasting (LTSF). Yet, our understanding of the reasons for their effectiveness remains limited. This work proposes a new way to understand self-attention networks: we have shown empirically that the entire attention mechanism in the encoder can be reduced to an MLP formed by feedforward, skip-connection, and layer normalization operations for temporal and/or spatial modeling in multivariate time series forecasting. Specifically, the Q, K, and V projection, the attention score calculation, the dot-product between the attention score and the V, and the final projection can be removed from the attention-based networks without significantly degrading the performance that the given network remains the top-tier compared to other SOTA methods. For spatio-temporal networks, the MLP-replace-attention network achieves a reduction in FLOPS of $62.579\%$ with a loss in performance less than $2.5\%$; for LTSF, a reduction in FLOPs of $42.233\%$ with a loss in performance less than $2\%$.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/eef80304bcbf017f066c662d896014bafb4b6cbb" target='_blank'>
@@ -1144,82 +1189,97 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2024-10-13</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>18</td>
         </tr>
 
-        <tr id="Multivariate time-series data in fields like healthcare and industry are informative but challenging due to high dimensionality and lack of labels. Recent self-supervised learning methods excel in learning rich representations without labels but struggle with disentangled embeddings and inductive bias issues like transformation-invariance. To address these challenges, we introduce TimeDRL, a framework for multivariate time-series representation learning with dual-level disentangled embeddings. TimeDRL features: (i) disentangled timestamp-level and instance-level embeddings using a [CLS] token strategy; (ii) timestamp-predictive and instance-contrastive tasks for representation learning; and (iii) avoidance of augmentation methods to eliminate inductive biases. Experiments on forecasting and classification datasets show TimeDRL outperforms existing methods, with further validation in semi-supervised settings with limited labeled data.">
+        <tr id="While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting. However, forecasters based on Transformers are still suffering from vulnerability to high-frequency signals, efficiency in computation, and bottleneck in full-spectrum utilization, which essentially are the cornerstones for accurately predicting time series with thousands of points. In this paper, we explore a novel perspective of enlightening signal processing for deep time series forecasting. Inspired by the filtering process, we introduce one simple yet effective network, namely FilterNet, built upon our proposed learnable frequency filters to extract key informative temporal patterns by selectively passing or attenuating certain components of time series signals. Concretely, we propose two kinds of learnable filters in the FilterNet: (i) Plain shaping filter, that adopts a universal frequency kernel for signal filtering and temporal modeling; (ii) Contextual shaping filter, that utilizes filtered frequencies examined in terms of its compatibility with input signals for dependency learning. Equipped with the two filters, FilterNet can approximately surrogate the linear and attention mappings widely adopted in time series literature, while enjoying superb abilities in handling high-frequency noises and utilizing the whole frequency spectrum that is beneficial for forecasting. Finally, we conduct extensive experiments on eight time series forecasting benchmarks, and experimental results have demonstrated our superior performance in terms of both effectiveness and efficiency compared with state-of-the-art methods. Code is available at this repository: https://github.com/aikunyi/FilterNet">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1f4084722f15df7440cf6161bcfc9aebe9fbf7cb" target='_blank'>
-              Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
+          <td><a href="https://www.semanticscholar.org/paper/3af83e96cc97a134738674d3167661ae710e5eb0" target='_blank'>
+              FilterNet: Harnessing Frequency Filters for Time Series Forecasting
               </a>
             </td>
           <td>
-            Ching Chang, Chiao-Tung Chan, Wei-Yao Wang, Wenjie Peng, Tien-Fu Chen
+            Kun Yi, Jingru Fei, Qi Zhang, Hui He, Shufeng Hao, Defu Lian, Wei Fan
           </td>
-          <td>2024-10-16</td>
+          <td>2024-11-03</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>7</td>
+          <td>6</td>
         </tr>
 
-        <tr id="Multivariate Time series forecasting is crucial in domains such as transportation, meteorology, and finance, especially for predicting extreme weather events. State-of-the-art methods predominantly rely on Transformer architectures, which utilize attention mechanisms to capture temporal dependencies. However, these methods are hindered by quadratic time complexity, limiting the model's scalability with respect to input sequence length. This significantly restricts their practicality in the real world. Mamba, based on state space models (SSM), provides a solution with linear time complexity, increasing the potential for efficient forecasting of sequential data. In this study, we propose UmambaTSF, a novel long-term time series forecasting framework that integrates multi-scale feature extraction capabilities of U-shaped encoder-decoder multilayer perceptrons (MLP) with Mamba's long sequence representation. To improve performance and efficiency, the Mamba blocks introduced in the framework adopt a refined residual structure and adaptable design, enabling the capture of unique temporal signals and flexible channel processing. In the experiments, UmambaTSF achieves state-of-the-art performance and excellent generality on widely used benchmark datasets while maintaining linear time complexity and low memory consumption.">
+        <tr id="Spatio-temporal forecasting is a critical component of various smart city applications, such as transportation optimization, energy management, and socio-economic analysis. Recently, several automated spatio-temporal forecasting methods have been proposed to automatically search the optimal neural network architecture for capturing complex spatio-temporal dependencies. However, the existing automated approaches suffer from expensive neural architecture search overhead, which hinders their practical use and the further exploration of diverse spatio-temporal operators in a finer granularity. In this paper, we propose AutoSTF, a decoupled automatic neural architecture search framework for cost-effective automated spatio-temporal forecasting. From the efficiency perspective, we first decouple the mixed search space into temporal space and spatial space and respectively devise representation compression and parameter-sharing schemes to mitigate the parameter explosion. The decoupled spatio-temporal search not only expedites the model optimization process but also leaves new room for more effective spatio-temporal dependency modeling. From the effectiveness perspective, we propose a multi-patch transfer module to jointly capture multi-granularity temporal dependencies and extend the spatial search space to enable finer-grained layer-wise spatial dependency search. Extensive experiments on eight datasets demonstrate the superiority of AutoSTF in terms of both accuracy and efficiency. Specifically, our proposed method achieves up to 13.48x speed-up compared to state-of-the-art automatic spatio-temporal forecasting methods while maintaining the best forecasting accuracy.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3d264e1c87378110d654ebbd6571cbe63c78f877" target='_blank'>
-              UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba
+          <td><a href="https://www.semanticscholar.org/paper/8c7796979329a74920ae324ef6374a0c6738d726" target='_blank'>
+              AutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting
               </a>
             </td>
           <td>
-            Li Wu, Wenbin Pei, Jiulong Jiao, Qiang Zhang
+            Tengfei Lyu, Weijiao Zhang, Jinliang Deng, Hao Liu
           </td>
-          <td>2024-10-15</td>
+          <td>2024-09-25</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>7</td>
         </tr>
 
-        <tr id="Long-short range time series forecasting is essential for predicting future trends and patterns over extended periods. While deep learning models such as Transformers have made significant strides in advancing time series forecasting, they often encounter difficulties in capturing long-term dependencies and effectively managing sparse semantic features. The state-space model, Mamba, addresses these issues through its adept handling of selective input and parallel computing, striking a balance between computational efficiency and prediction accuracy. This article examines the advantages and disadvantages of both Mamba and Transformer models, and introduces a combined approach, MAT, which leverages the strengths of each model to capture unique long-short range dependencies and inherent evolutionary patterns in multivariate time series. Specifically, MAT harnesses the long-range dependency capabilities of Mamba and the short-range characteristics of Transformers. Experimental results on benchmark weather datasets demonstrate that MAT outperforms existing comparable methods in terms of prediction accuracy, scalability, and memory efficiency.">
+        <tr id="Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons. Despite the recent strides in crafting specific architectures for time-series forecasting and developing pre-trained universal models, a comprehensive examination of their capability in accommodating varied-horizon forecasting during inference is still lacking. This paper bridges this gap through the design and evaluation of the Elastic Time-Series Transformer (ElasTST). The ElasTST model incorporates a non-autoregressive design with placeholders and structured self-attention masks, warranting future outputs that are invariant to adjustments in inference horizons. A tunable version of rotary position embedding is also integrated into ElasTST to capture time-series-specific periods and enhance adaptability to different horizons. Additionally, ElasTST employs a multi-scale patch design, effectively integrating both fine-grained and coarse-grained information. During the training phase, ElasTST uses a horizon reweighting strategy that approximates the effect of random sampling across multiple horizons with a single fixed horizon setting. Through comprehensive experiments and comparisons with state-of-the-art time-series architectures and contemporary foundation models, we demonstrate the efficacy of ElasTST's unique design elements. Our findings position ElasTST as a robust solution for the practical necessity of varied-horizon forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/3e0aa05e9c0ee4fc6b3c67887960a9ef18c502d4" target='_blank'>
-              Integration of Mamba and Transformer - MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/1655d8b0d53bfdc28d81fbb6aa765118ac291460" target='_blank'>
+              ElasTST: Towards Robust Varied-Horizon Forecasting with Elastic Time-Series Transformer
               </a>
             </td>
           <td>
-            Wenqing Zhang, Junming Huang, Ruotong Wang, Changsong Wei, Wenqian Huang, 
+            Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou, Jiang Bian, Jia Li
           </td>
-          <td>2024-09-13</td>
+          <td>2024-11-04</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>2</td>
+          <td>8</td>
         </tr>
 
-        <tr id="Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://github.com/shangzongjiang/Ada-MSHyper.">
+        <tr id="Multivariate time-series data in fields like healthcare and industry are informative but challenging due to high dimensionality and lack of labels. Recent self-supervised learning methods excel in learning rich representations without labels but struggle with disentangled embeddings and inductive bias issues like transformation-invariance. To address these challenges, we introduce TimeDRL, a framework for multivariate time-series representation learning with dual-level disentangled embeddings. TimeDRL features: (i) disentangled timestamp-level and instance-level embeddings using a [CLS] token strategy; (ii) timestamp-predictive and instance-contrastive tasks for representation learning; and (iii) avoidance of augmentation methods to eliminate inductive biases. Experiments on forecasting and classification datasets show TimeDRL outperforms existing methods, with further validation in semi-supervised settings with limited labeled data.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a474e4f612c98f409325d6413529652d1c630603" target='_blank'>
-              Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/1f4084722f15df7440cf6161bcfc9aebe9fbf7cb" target='_blank'>
+              Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
               </a>
             </td>
           <td>
-            Zongjiang Shang, Ling Chen, Binqing Wu, Dongliang Cui
+            Ching Chang, Chiao-Tung Chan, Wei-Yao Wang, Wenjie Peng, Tien-Fu Chen
           </td>
-          <td>2024-10-31</td>
+          <td>2024-10-16</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>5</td>
+          <td>7</td>
         </tr>
 
-        <tr id="Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-distribution (ID) data, leaving their out-of-distribution (OOD) scaling behavior and the influence of model architectures less explored. In this work, we examine two common TSFM architectures, encoder-only and decoder-only Transformers, and investigate their scaling behavior on both ID and OOD data. These models are trained and evaluated across varying parameter counts, compute budgets, and dataset sizes. Our experiments reveal that the log-likelihood loss of TSFMs exhibits similar scaling behavior in both OOD and ID settings. We further compare the scaling properties across different architectures, incorporating two state-of-the-art TSFMs as case studies, showing that model architecture plays a significant role in scaling. The encoder-only Transformers demonstrate better scalability than the decoder-only Transformers, while the architectural enhancements in the two advanced TSFMs primarily improve ID performance but reduce OOD scalability. While scaling up TSFMs is expected to drive performance breakthroughs, the lack of a comprehensive understanding of TSFM scaling laws has hindered the development of a robust framework to guide model scaling. We fill this gap in this work by synthesizing our findings and providing practical guidelines for designing and scaling larger TSFMs with enhanced model capabilities.">
+        <tr id="Although the Transformer-based models have been the dominant architecture for long-term time series forecasting tasks in recent years, there is an essential problem. Most of these methods adopt the standard self-attention mechanism which not only has a high computational complexity, but also fails to capture the local and global dependencies from historical time series data simultaneously. In this paper, we propose CIPformer, a novel Transformer-based model for long-term time series forecasting. It utilizes channel independence, where each channel comprises a solitary univariate time series, sharing identical embedding and Transformer weights across all series. Additionally, patch embedding is employed to extract the local information and reduce time and memory complexity of the attention mechanism quadratically. With a deftly designed patch-wise attention which encompasses both intra-patch and inter-patch attention, CIPformer empowers the Transformer encoder with the ability to capture both local features and global interactions. Furthermore, in order to learn linear information, we employ a linear model to better capture linear trends and details. Extensive experiments conducted on six real-world datasets have demonstrated that our proposed model CIPformer can enhance the multivariate long-term time series forecasting performance, when compared with some of the state-of-the-art Transformer-based methods.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a87d911bee64f961730142670dadf9f5b8cc9210" target='_blank'>
-              Towards Neural Scaling Laws for Time Series Foundation Models
+          <td><a href="https://www.semanticscholar.org/paper/82bc5a9a2af84ca0044dcf9fee4154941af7e9a1" target='_blank'>
+              CIPformer: Channel-Independence Integrated with Patching Transformer for Multivariate Long-Term Time Series Forecasting
               </a>
             </td>
           <td>
-            Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin, Shirui Pan
+            Peng Qiu, Jinhong Li
           </td>
-          <td>2024-10-16</td>
+          <td>2024-09-20</td>
+          <td>2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC)</td>
+          <td>0</td>
+          <td>0</td>
+        </tr>
+
+        <tr id="Multivariate Time series forecasting is crucial in domains such as transportation, meteorology, and finance, especially for predicting extreme weather events. State-of-the-art methods predominantly rely on Transformer architectures, which utilize attention mechanisms to capture temporal dependencies. However, these methods are hindered by quadratic time complexity, limiting the model's scalability with respect to input sequence length. This significantly restricts their practicality in the real world. Mamba, based on state space models (SSM), provides a solution with linear time complexity, increasing the potential for efficient forecasting of sequential data. In this study, we propose UmambaTSF, a novel long-term time series forecasting framework that integrates multi-scale feature extraction capabilities of U-shaped encoder-decoder multilayer perceptrons (MLP) with Mamba's long sequence representation. To improve performance and efficiency, the Mamba blocks introduced in the framework adopt a refined residual structure and adaptable design, enabling the capture of unique temporal signals and flexible channel processing. In the experiments, UmambaTSF achieves state-of-the-art performance and excellent generality on widely used benchmark datasets while maintaining linear time complexity and low memory consumption.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/3d264e1c87378110d654ebbd6571cbe63c78f877" target='_blank'>
+              UmambaTSF: A U-shaped Multi-Scale Long-Term Time Series Forecasting Method Using Mamba
+              </a>
+            </td>
+          <td>
+            Li Wu, Wenbin Pei, Jiulong Jiao, Qiang Zhang
+          </td>
+          <td>2024-10-15</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>6</td>
+          <td>0</td>
         </tr>
 
         <tr id="Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. Traditional time series approaches for prediction often focus on either autoregressive modeling, which relies solely on past observations of the target ``endogenous variables'', or forward modeling, which considers only current covariate drivers ``exogenous variables''. However, effectively integrating past endogenous and past exogenous with current exogenous variables remains a significant challenge. In this paper, we propose ExoTST, a novel transformer-based framework that effectively incorporates current exogenous variables alongside past context for improved time series prediction. To integrate exogenous information efficiently, ExoTST leverages the strengths of attention mechanisms and introduces a novel cross-temporal modality fusion module. This module enables the model to jointly learn from both past and current exogenous series, treating them as distinct modalities. By considering these series separately, ExoTST provides robustness and flexibility in handling data uncertainties that arise from the inherent distribution shift between historical and current exogenous variables. Extensive experiments on real-world carbon flux datasets and time series benchmarks demonstrate ExoTST's superior performance compared to state-of-the-art baselines, with improvements of up to 10\% in prediction accuracy. Moreover, ExoTST exhibits strong robustness against missing values and noise in exogenous drivers, maintaining consistent performance in real-world situations where these imperfections are common.">
@@ -1237,76 +1297,76 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>19</td>
         </tr>
 
-        <tr id="Spatiotemporal (ST) graph modeling has garnered increasing attention recently. Most existing methods rely on a predefined graph structure or construct a single learnable graph throughout training. However, it is challenging to use a predefined graph structure to capture dynamic ST changes effectively due to evolving node relationships over time. Furthermore, these methods typically utilize only the original data, neglecting external temporal factors. Therefore, we put forward a novel time-varying graph convolutional network model that integrates external factors for multifeature ST series prediction. Firstly, we construct a time-varying adjacency matrix using attention to capture dynamic spatial relationships among nodes. The graph structure adapts over time during training, validation, and testing phases. Then, we model temporal dependence by dilated causal convolution, leveraging gated activation unit and residual connection. Notably, the prediction accuracy is enhanced through the incorporation of embedding absolute time and the fusion of multifeature. This model has been applied to three real-world multifeature datasets, achieving state-of-the-art performance in all cases. Experiments show that the method has high accuracy and robustness when applied to multifeature and multivariate ST series problems.">
+        <tr id="Long-short range time series forecasting is essential for predicting future trends and patterns over extended periods. While deep learning models such as Transformers have made significant strides in advancing time series forecasting, they often encounter difficulties in capturing long-term dependencies and effectively managing sparse semantic features. The state-space model, Mamba, addresses these issues through its adept handling of selective input and parallel computing, striking a balance between computational efficiency and prediction accuracy. This article examines the advantages and disadvantages of both Mamba and Transformer models, and introduces a combined approach, MAT, which leverages the strengths of each model to capture unique long-short range dependencies and inherent evolutionary patterns in multivariate time series. Specifically, MAT harnesses the long-range dependency capabilities of Mamba and the short-range characteristics of Transformers. Experimental results on benchmark weather datasets demonstrate that MAT outperforms existing comparable methods in terms of prediction accuracy, scalability, and memory efficiency.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8767500a1efaa55cc4c19167ae35e6f2419f2b6a" target='_blank'>
-              TVGCN: Time-varying graph convolutional networks for multivariate and multifeature spatiotemporal series prediction
+          <td><a href="https://www.semanticscholar.org/paper/3e0aa05e9c0ee4fc6b3c67887960a9ef18c502d4" target='_blank'>
+              Integration of Mamba and Transformer - MAT for Long-Short Range Time Series Forecasting with Application to Weather Dynamics
               </a>
             </td>
           <td>
-            Feiyan Sun, Wenning Hao, Ao Zou, Kai Cheng
+            Wenqing Zhang, Junming Huang, Ruotong Wang, Changsong Wei, Wenqian Huang, 
           </td>
-          <td>2024-07-01</td>
-          <td>Science Progress</td>
+          <td>2024-09-13</td>
+          <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
         </tr>
 
-        <tr id="Multivariate time series (MTS) forecasting plays a crucial role in various real-world applications, yet simultaneously capturing both temporal and inter-variable dependencies remains a challenge. Conventional Channel-Dependent (CD) models handle these dependencies separately, limiting their ability to model complex interactions such as lead-lag dynamics. To address these limitations, we propose TiVaT (Time-Variable Transformer), a novel architecture that integrates temporal and variate dependencies through its Joint-Axis (JA) attention mechanism. TiVaT's ability to capture intricate variate-temporal dependencies, including asynchronous interactions, is further enhanced by the incorporation of Distance-aware Time-Variable (DTV) Sampling, which reduces noise and improves accuracy through a learned 2D map that focuses on key interactions. TiVaT effectively models both temporal and variate dependencies, consistently delivering strong performance across diverse datasets. Notably, it excels in capturing complex patterns within multivariate time series, enabling it to surpass or remain competitive with state-of-the-art methods. This positions TiVaT as a new benchmark in MTS forecasting, particularly in handling datasets characterized by intricate and challenging dependencies.">
+        <tr id="Scaling laws offer valuable insights into the design of time series foundation models (TSFMs). However, previous research has largely focused on the scaling laws of TSFMs for in-distribution (ID) data, leaving their out-of-distribution (OOD) scaling behavior and the influence of model architectures less explored. In this work, we examine two common TSFM architectures, encoder-only and decoder-only Transformers, and investigate their scaling behavior on both ID and OOD data. These models are trained and evaluated across varying parameter counts, compute budgets, and dataset sizes. Our experiments reveal that the log-likelihood loss of TSFMs exhibits similar scaling behavior in both OOD and ID settings. We further compare the scaling properties across different architectures, incorporating two state-of-the-art TSFMs as case studies, showing that model architecture plays a significant role in scaling. The encoder-only Transformers demonstrate better scalability than the decoder-only Transformers, while the architectural enhancements in the two advanced TSFMs primarily improve ID performance but reduce OOD scalability. While scaling up TSFMs is expected to drive performance breakthroughs, the lack of a comprehensive understanding of TSFM scaling laws has hindered the development of a robust framework to guide model scaling. We fill this gap in this work by synthesizing our findings and providing practical guidelines for designing and scaling larger TSFMs with enhanced model capabilities.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8959127abeb1daeffd9ee3243b463d8b434881db" target='_blank'>
-              TiVaT: Joint-Axis Attention for Time Series Forecasting with Lead-Lag Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/a87d911bee64f961730142670dadf9f5b8cc9210" target='_blank'>
+              Towards Neural Scaling Laws for Time Series Foundation Models
               </a>
             </td>
           <td>
-            Junwoo Ha, Hyukjae Kwon, Sungsoo Kim, Kisu Lee, Ha Young Kim
+            Qingren Yao, Chao-Han Huck Yang, Renhe Jiang, Yuxuan Liang, Ming Jin, Shirui Pan
           </td>
-          <td>2024-10-02</td>
+          <td>2024-10-16</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>3</td>
+          <td>6</td>
         </tr>
 
-        <tr id="Multivariate time series forecasting relies on accurately capturing the correlations among variates. Current channel-independent (CI) models and models with a CI final projection layer are unable to capture these dependencies. In this paper, we present the variate embedding (VE) pipeline, which learns a unique and consistent embedding for each variate and combines it with Mixture of Experts (MoE) and Low-Rank Adaptation (LoRA) techniques to enhance forecasting performance while controlling parameter size. The VE pipeline can be integrated into any model with a CI final projection layer to improve multivariate forecasting. The learned VE effectively groups variates with similar temporal patterns and separates those with low correlations. The effectiveness of the VE pipeline is demonstrated through experiments on four widely-used datasets. The code is available at: https://github.com/swang-song/VE.">
+        <tr id="Spatiotemporal (ST) graph modeling has garnered increasing attention recently. Most existing methods rely on a predefined graph structure or construct a single learnable graph throughout training. However, it is challenging to use a predefined graph structure to capture dynamic ST changes effectively due to evolving node relationships over time. Furthermore, these methods typically utilize only the original data, neglecting external temporal factors. Therefore, we put forward a novel time-varying graph convolutional network model that integrates external factors for multifeature ST series prediction. Firstly, we construct a time-varying adjacency matrix using attention to capture dynamic spatial relationships among nodes. The graph structure adapts over time during training, validation, and testing phases. Then, we model temporal dependence by dilated causal convolution, leveraging gated activation unit and residual connection. Notably, the prediction accuracy is enhanced through the incorporation of embedding absolute time and the fusion of multifeature. This model has been applied to three real-world multifeature datasets, achieving state-of-the-art performance in all cases. Experiments show that the method has high accuracy and robustness when applied to multifeature and multivariate ST series problems.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/18d73f2abcef586684b4f585111ebb4e4945df94" target='_blank'>
-              VE: Modeling Multivariate Time Series Correlation with Variate Embedding
+          <td><a href="https://www.semanticscholar.org/paper/8767500a1efaa55cc4c19167ae35e6f2419f2b6a" target='_blank'>
+              TVGCN: Time-varying graph convolutional networks for multivariate and multifeature spatiotemporal series prediction
               </a>
             </td>
           <td>
-            Shangjiong Wang, Zhihong Man, Zhengwei Cao, Jinchuan Zheng, Zhikang Ge
+            Feiyan Sun, Wenning Hao, Ao Zou, Kai Cheng
           </td>
-          <td>2024-09-10</td>
-          <td>ArXiv</td>
+          <td>2024-07-01</td>
+          <td>Science Progress</td>
           <td>0</td>
-          <td>3</td>
+          <td>2</td>
         </tr>
 
-        <tr id="Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4\%, 94.9\%, 60\% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git">
+        <tr id="Multivariate time series (MTS) forecasting plays a crucial role in various real-world applications, yet simultaneously capturing both temporal and inter-variable dependencies remains a challenge. Conventional Channel-Dependent (CD) models handle these dependencies separately, limiting their ability to model complex interactions such as lead-lag dynamics. To address these limitations, we propose TiVaT (Time-Variable Transformer), a novel architecture that integrates temporal and variate dependencies through its Joint-Axis (JA) attention mechanism. TiVaT's ability to capture intricate variate-temporal dependencies, including asynchronous interactions, is further enhanced by the incorporation of Distance-aware Time-Variable (DTV) Sampling, which reduces noise and improves accuracy through a learned 2D map that focuses on key interactions. TiVaT effectively models both temporal and variate dependencies, consistently delivering strong performance across diverse datasets. Notably, it excels in capturing complex patterns within multivariate time series, enabling it to surpass or remain competitive with state-of-the-art methods. This positions TiVaT as a new benchmark in MTS forecasting, particularly in handling datasets characterized by intricate and challenging dependencies.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/395ddd7773e4b19dfb97558d5903354d1124c4be" target='_blank'>
-              Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
+          <td><a href="https://www.semanticscholar.org/paper/8959127abeb1daeffd9ee3243b463d8b434881db" target='_blank'>
+              TiVaT: Joint-Axis Attention for Time Series Forecasting with Lead-Lag Dynamics
               </a>
             </td>
           <td>
-            L. Zheng, Zhengyang Li, C. Dong, W. Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
+            Junwoo Ha, Hyukjae Kwon, Sungsoo Kim, Kisu Lee, Ha Young Kim
           </td>
-          <td>2024-10-16</td>
-          <td>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management</td>
+          <td>2024-10-02</td>
+          <td>ArXiv</td>
           <td>0</td>
-          <td>7</td>
+          <td>3</td>
         </tr>
 
-        <tr id="We propose an Autoregressive (AR) Moving-average (MA) attention structure that can adapt to various linear attention mechanisms, enhancing their ability to capture long-range and local temporal patterns in time series. In this paper, we first demonstrate that, for the time series forecasting (TSF) task, the previously overlooked decoder-only autoregressive Transformer model can achieve results comparable to the best baselines when appropriate tokenization and training methods are applied. Moreover, inspired by the ARMA model from statistics and recent advances in linear attention, we introduce the full ARMA structure into existing autoregressive attention mechanisms. By using an indirect MA weight generation method, we incorporate the MA term while maintaining the time complexity and parameter size of the underlying efficient attention models. We further explore how indirect parameter generation can produce implicit MA weights that align with the modeling requirements for local temporal impacts. Experimental results show that incorporating the ARMA structure consistently improves the performance of various AR attentions on TSF tasks, achieving state-of-the-art results.">
+        <tr id="Mamba has recently emerged as a promising alternative to Transformers, offering near-linear complexity in processing sequential data. However, while channels in time series (TS) data have no specific order in general, recent studies have adopted Mamba to capture channel dependencies (CD) in TS, introducing a sequential order bias. To address this issue, we propose SOR-Mamba, a TS forecasting method that 1) incorporates a regularization strategy to minimize the discrepancy between two embedding vectors generated from data with reversed channel orders, thereby enhancing robustness to channel order, and 2) eliminates the 1D-convolution originally designed to capture local information in sequential data. Furthermore, we introduce channel correlation modeling (CCM), a pretraining task aimed at preserving correlations between channels from the data space to the latent space in order to enhance the ability to capture CD. Extensive experiments demonstrate the efficacy of the proposed method across standard and transfer learning scenarios. Code is available at https://github.com/seunghan96/SOR-Mamba.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/38269db5cf2621d020be004caca5919c83acffa8" target='_blank'>
-              Autoregressive Moving-average Attention Mechanism for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/4315bb0130a324bd6212bd8c5d1f629675aed88e" target='_blank'>
+              Sequential Order-Robust Mamba for Time Series Forecasting
               </a>
             </td>
           <td>
-            Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang
+            Seunghan Lee, Juri Hong, Kibok Lee, Taeyoung Park
           </td>
-          <td>2024-10-04</td>
+          <td>2024-10-30</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
@@ -1327,46 +1387,46 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>5</td>
         </tr>
 
-        <tr id="In recent years, large language models (LLMs) have shown great potential in time-series analysis by capturing complex dependencies and improving predictive performance. However, existing approaches often struggle with modality alignment, leading to suboptimal results. To address these challenges, we present a novel framework, TS-TCD, which introduces a comprehensive three-tiered cross-modal knowledge distillation mechanism. Unlike prior work that focuses on isolated alignment techniques, our framework systematically integrates: 1) Dynamic Adaptive Gating for Input Encoding and Alignment}, ensuring coherent alignment between time-series tokens and QR-decomposed textual embeddings; 2) Layer-Wise Contrastive Learning}, aligning intermediate representations across modalities to reduce feature-level discrepancies; and 3) Optimal Transport-Driven Output Alignment}, which ensures consistent output predictions through fine-grained cross-modal alignment. Extensive experiments on benchmark time-series datasets demonstrate that TS-TCD achieves state-of-the-art results, outperforming traditional methods in both accuracy and robustness.">
+        <tr id="Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4\%, 94.9\%, 60\% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/5b3f3b64cd3b543ad8317954ebd97c7570f0449a" target='_blank'>
-              TS-TCD: Triplet-Level Cross-Modal Distillation for Time-Series Forecasting Using Large Language Models
+          <td><a href="https://www.semanticscholar.org/paper/395ddd7773e4b19dfb97558d5903354d1124c4be" target='_blank'>
+              Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
               </a>
             </td>
           <td>
-            Pengfei Wang, Huanran Zheng, Silong Dai, Wenjing Yue, Wei Zhu, Xiaoling Wang
+            L. Zheng, Zhengyang Li, C. Dong, W. Zhang, Lin Yue, Miao Xu, Olaf Maennel, Weitong Chen
           </td>
-          <td>2024-09-23</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>3</td>
+          <td>2024-10-16</td>
+          <td>Proceedings of the 33rd ACM International Conference on Information and Knowledge Management</td>
+          <td>0</td>
+          <td>7</td>
         </tr>
 
-        <tr id="Time series forecasting is extensively applied across diverse domains. Transformer-based models demonstrate significant potential in modeling cross-time and cross-variable interaction. However, we notice that the cross-variable correlation of multivariate time series demonstrates multifaceted (positive and negative correlations) and dynamic progression over time, which is not well captured by existing Transformer-based models. To address this issue, we propose a TimeCNN model to refine cross-variable interactions to enhance time series forecasting. Its key innovation is timepoint-independent, where each time point has an independent convolution kernel, allowing each time point to have its independent model to capture relationships among variables. This approach effectively handles both positive and negative correlations and adapts to the evolving nature of variable relationships over time. Extensive experiments conducted on 12 real-world datasets demonstrate that TimeCNN consistently outperforms state-of-the-art models. Notably, our model achieves significant reductions in computational requirements (approximately 60.46%) and parameter count (about 57.50%), while delivering inference speeds 3 to 4 times faster than the benchmark iTransformer model">
+        <tr id="In recent years, large language models (LLMs) have shown great potential in time-series analysis by capturing complex dependencies and improving predictive performance. However, existing approaches often struggle with modality alignment, leading to suboptimal results. To address these challenges, we present a novel framework, TS-TCD, which introduces a comprehensive three-tiered cross-modal knowledge distillation mechanism. Unlike prior work that focuses on isolated alignment techniques, our framework systematically integrates: 1) Dynamic Adaptive Gating for Input Encoding and Alignment}, ensuring coherent alignment between time-series tokens and QR-decomposed textual embeddings; 2) Layer-Wise Contrastive Learning}, aligning intermediate representations across modalities to reduce feature-level discrepancies; and 3) Optimal Transport-Driven Output Alignment}, which ensures consistent output predictions through fine-grained cross-modal alignment. Extensive experiments on benchmark time-series datasets demonstrate that TS-TCD achieves state-of-the-art results, outperforming traditional methods in both accuracy and robustness.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/70716b8b8c42001bd9093d3b115ac80b3f06da85" target='_blank'>
-              TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/5b3f3b64cd3b543ad8317954ebd97c7570f0449a" target='_blank'>
+              TS-TCD: Triplet-Level Cross-Modal Distillation for Time-Series Forecasting Using Large Language Models
               </a>
             </td>
           <td>
-            Ao Hu, Dongkai Wang, Yong Dai, Shiyi Qi, Liangjian Wen, Jun Wang, Zhi Chen, Xun Zhou, Zenglin Xu, Jiang Duan
+            Pengfei Wang, Huanran Zheng, Silong Dai, Wenjing Yue, Wei Zhu, Xiaoling Wang
           </td>
-          <td>2024-10-07</td>
+          <td>2024-09-23</td>
           <td>ArXiv</td>
-          <td>0</td>
+          <td>1</td>
           <td>3</td>
         </tr>
 
-        <tr id="In many domains, such as healthcare, time-series data is often irregularly sampled with varying intervals between observations. This poses challenges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called Trajectory Generative Pre-trained Transformer (TrajGPT). TrajGPT employs a novel Selective Recurrent Attention (SRA) mechanism, which utilizes a data-dependent decay to adaptively filter out irrelevant past information based on contexts. By interpreting TrajGPT as discretized ordinary differential equations (ODEs), it effectively captures the underlying continuous dynamics and enables time-specific inference for forecasting arbitrary target timesteps. Experimental results demonstrate that TrajGPT excels in trajectory forecasting, drug usage prediction, and phenotype classification without requiring task-specific fine-tuning. By evolving the learned continuous dynamics, TrajGPT can interpolate and extrapolate disease risk trajectories from partially-observed time series. The visualization of predicted health trajectories shows that TrajGPT forecasts unseen diseases based on the history of clinically relevant phenotypes (i.e., contexts).">
+        <tr id="We propose an Autoregressive (AR) Moving-average (MA) attention structure that can adapt to various linear attention mechanisms, enhancing their ability to capture long-range and local temporal patterns in time series. In this paper, we first demonstrate that, for the time series forecasting (TSF) task, the previously overlooked decoder-only autoregressive Transformer model can achieve results comparable to the best baselines when appropriate tokenization and training methods are applied. Moreover, inspired by the ARMA model from statistics and recent advances in linear attention, we introduce the full ARMA structure into existing autoregressive attention mechanisms. By using an indirect MA weight generation method, we incorporate the MA term while maintaining the time complexity and parameter size of the underlying efficient attention models. We further explore how indirect parameter generation can produce implicit MA weights that align with the modeling requirements for local temporal impacts. Experimental results show that incorporating the ARMA structure consistently improves the performance of various AR attentions on TSF tasks, achieving state-of-the-art results.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1de2eba7172509c524a8adacc9179f5bae225bb1" target='_blank'>
-              TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
+          <td><a href="https://www.semanticscholar.org/paper/38269db5cf2621d020be004caca5919c83acffa8" target='_blank'>
+              Autoregressive Moving-average Attention Mechanism for Time Series Forecasting
               </a>
             </td>
           <td>
-            Ziyang Song, Qingcheng Lu, He Zhu, David L. Buckeridge, Yue Li
+            Jiecheng Lu, Xu Han, Yan Sun, Shihao Yang
           </td>
-          <td>2024-10-03</td>
+          <td>2024-10-04</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
@@ -1387,46 +1447,31 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>1</td>
         </tr>
 
-        <tr id="Forecasting models are pivotal in a data-driven world with vast volumes of time series data that appear as a compound of vast Linear and Nonlinear patterns. Recent deep time series forecasting models struggle to utilize seasonal and trend decomposition to separate the entangled components. Such a strategy only explicitly extracts simple linear patterns like trends, leaving the other linear modes and vast unexplored nonlinear patterns to the residual. Their flawed linear and nonlinear feature extraction models and shallow-level decomposition limit their adaptation to the diverse patterns present in real-world scenarios. Given this, we innovate Recursive Residual Decomposition by introducing explicit extraction of both linear and nonlinear patterns. This deeper-level decomposition framework, which is named LiNo, captures linear patterns using a Li block which can be a moving average kernel, and models nonlinear patterns using a No block which can be a Transformer encoder. The extraction of these two patterns is performed alternatively and recursively. To achieve the full potential of LiNo, we develop the current simple linear pattern extractor to a general learnable autoregressive model, and design a novel No block that can handle all essential nonlinear patterns. Remarkably, the proposed LiNo achieves state-of-the-art on thirteen real-world benchmarks under univariate and multivariate forecasting scenarios. Experiments show that current forecasting models can deliver more robust and precise results through this advanced Recursive Residual Decomposition. We hope this work could offer insight into designing more effective forecasting models. Code is available at this Repository: https://github.com/Levi-Ackman/LiNo.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/7871420ed5b3abdc8d7aed30ab3ca518e9f85987" target='_blank'>
-              LiNo: Advancing Recursive Residual Decomposition of Linear and Nonlinear Patterns for Robust Time Series Forecasting
-              </a>
-            </td>
-          <td>
-            Guoqi Yu, Yaoming Li, Xiaoyu Guo, Dayu Wang, Zirui Liu, Shujun Wang, Tong Yang
-          </td>
-          <td>2024-10-22</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>3</td>
-        </tr>
-
-        <tr id="Time series~(TS) modeling is essential in dynamic systems like weather prediction and anomaly detection. Recent studies utilize Large Language Models (LLMs) for TS modeling, leveraging their powerful pattern recognition capabilities. These methods primarily position LLMs as the predictive backbone, often omitting the mathematical modeling within traditional TS models, such as periodicity. However, disregarding the potential of LLMs also overlooks their pattern recognition capabilities. To address this gap, we introduce \textit{LLM-TS Integrator}, a novel framework that effectively integrates the capabilities of LLMs into traditional TS modeling. Central to this integration is our \textit{mutual information} module. The core of this \textit{mutual information} module is a traditional TS model enhanced with LLM-derived insights for improved predictive abilities. This enhancement is achieved by maximizing the mutual information between traditional model's TS representations and LLM's textual representation counterparts, bridging the two modalities. Moreover, we recognize that samples vary in importance for two losses: traditional prediction and mutual information maximization. To address this variability, we introduce the \textit{sample reweighting} module to improve information utilization. This module assigns dual weights to each sample: one for prediction loss and another for mutual information loss, dynamically optimizing these weights via bi-level optimization. Our method achieves state-of-the-art or comparable performance across five mainstream TS tasks, including short-term and long-term forecasting, imputation, classification, and anomaly detection.">
+        <tr id="In many domains, such as healthcare, time-series data is often irregularly sampled with varying intervals between observations. This poses challenges for classical time-series models that require equally spaced data. To address this, we propose a novel time-series Transformer called Trajectory Generative Pre-trained Transformer (TrajGPT). TrajGPT employs a novel Selective Recurrent Attention (SRA) mechanism, which utilizes a data-dependent decay to adaptively filter out irrelevant past information based on contexts. By interpreting TrajGPT as discretized ordinary differential equations (ODEs), it effectively captures the underlying continuous dynamics and enables time-specific inference for forecasting arbitrary target timesteps. Experimental results demonstrate that TrajGPT excels in trajectory forecasting, drug usage prediction, and phenotype classification without requiring task-specific fine-tuning. By evolving the learned continuous dynamics, TrajGPT can interpolate and extrapolate disease risk trajectories from partially-observed time series. The visualization of predicted health trajectories shows that TrajGPT forecasts unseen diseases based on the history of clinically relevant phenotypes (i.e., contexts).">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/013fefa7c76d4f07cd5b40bb12553cb17be9e98c" target='_blank'>
-              LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
+          <td><a href="https://www.semanticscholar.org/paper/1de2eba7172509c524a8adacc9179f5bae225bb1" target='_blank'>
+              TrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
               </a>
             </td>
           <td>
-            Can Chen, Gabriel Oliveira, H. S. Noghabi, Tristan Sylvain
+            Ziyang Song, Qingcheng Lu, He Zhu, David L. Buckeridge, Yue Li
           </td>
-          <td>2024-10-21</td>
+          <td>2024-10-03</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
         </tr>
 
-        <tr id="Leveraging large language models (LLMs) has garnered increasing attention and introduced novel perspectives in time series classification. However, existing approaches often overlook the crucial dynamic temporal information inherent in time series data and face challenges in aligning this data with textual semantics. To address these limitations, we propose HiTime, a hierarchical multi-modal model that seamlessly integrates temporal information into LLMs for multivariate time series classification (MTSC). Our model employs a hierarchical feature encoder to capture diverse aspects of time series data through both data-specific and task-specific embeddings. To facilitate semantic space alignment between time series and text, we introduce a dual-view contrastive alignment module that bridges the gap between modalities. Additionally, we adopt a hybrid prompting strategy to fine-tune the pre-trained LLM in a parameter-efficient manner. By effectively incorporating dynamic temporal features and ensuring semantic alignment, HiTime enables LLMs to process continuous time series data and achieves state-of-the-art classification performance through text generation. Extensive experiments on benchmark datasets demonstrate that HiTime significantly enhances time series classification accuracy compared to most competitive baseline methods. Our findings highlight the potential of integrating temporal features into LLMs, paving the way for advanced time series analysis. The code is publicly available for further research and validation. Our codes are publicly available1.">
+        <tr id="Time series forecasting is extensively applied across diverse domains. Transformer-based models demonstrate significant potential in modeling cross-time and cross-variable interaction. However, we notice that the cross-variable correlation of multivariate time series demonstrates multifaceted (positive and negative correlations) and dynamic progression over time, which is not well captured by existing Transformer-based models. To address this issue, we propose a TimeCNN model to refine cross-variable interactions to enhance time series forecasting. Its key innovation is timepoint-independent, where each time point has an independent convolution kernel, allowing each time point to have its independent model to capture relationships among variables. This approach effectively handles both positive and negative correlations and adapts to the evolving nature of variable relationships over time. Extensive experiments conducted on 12 real-world datasets demonstrate that TimeCNN consistently outperforms state-of-the-art models. Notably, our model achieves significant reductions in computational requirements (approximately 60.46%) and parameter count (about 57.50%), while delivering inference speeds 3 to 4 times faster than the benchmark iTransformer model">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f491028ab69f126357a069afab903de3fad67f2f" target='_blank'>
-              Hierarchical Multimodal LLMs with Semantic Space Alignment for Enhanced Time Series Classification
+          <td><a href="https://www.semanticscholar.org/paper/70716b8b8c42001bd9093d3b115ac80b3f06da85" target='_blank'>
+              TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting
               </a>
             </td>
           <td>
-            Xiaoyu Tao, Tingyue Pan, Mingyue Cheng, Yucong Luo
+            Ao Hu, Dongkai Wang, Yong Dai, Shiyi Qi, Liangjian Wen, Jun Wang, Zhi Chen, Xun Zhou, Zenglin Xu, Jiang Duan
           </td>
-          <td>2024-10-24</td>
+          <td>2024-10-07</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>3</td>
@@ -1447,79 +1492,34 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
           <td>2</td>
         </tr>
 
-        <tr id="Recent research has challenged the necessity of complex deep learning architectures for time series forecasting, demonstrating that simple linear models can often outperform sophisticated approaches. Building upon this insight, we introduce a novel architecture the Temporal Linear Net (TLN), that extends the capabilities of linear models while maintaining interpretability and computational efficiency. TLN is designed to effectively capture both temporal and feature-wise dependencies in multivariate time series data. Our approach is a variant of TSMixer that maintains strict linearity throughout its architecture. TSMixer removes activation functions, introduces specialized kernel initializations, and incorporates dilated convolutions to handle various time scales, while preserving the linear nature of the model. Unlike transformer-based models that may lose temporal information due to their permutation-invariant nature, TLN explicitly preserves and leverages the temporal structure of the input data. A key innovation of TLN is its ability to compute an equivalent linear model, offering a level of interpretability not found in more complex architectures such as TSMixer. This feature allows for seamless conversion between the full TLN model and its linear equivalent, facilitating both training flexibility and inference optimization.">
+        <tr id="Time series~(TS) modeling is essential in dynamic systems like weather prediction and anomaly detection. Recent studies utilize Large Language Models (LLMs) for TS modeling, leveraging their powerful pattern recognition capabilities. These methods primarily position LLMs as the predictive backbone, often omitting the mathematical modeling within traditional TS models, such as periodicity. However, disregarding the potential of LLMs also overlooks their pattern recognition capabilities. To address this gap, we introduce \textit{LLM-TS Integrator}, a novel framework that effectively integrates the capabilities of LLMs into traditional TS modeling. Central to this integration is our \textit{mutual information} module. The core of this \textit{mutual information} module is a traditional TS model enhanced with LLM-derived insights for improved predictive abilities. This enhancement is achieved by maximizing the mutual information between traditional model's TS representations and LLM's textual representation counterparts, bridging the two modalities. Moreover, we recognize that samples vary in importance for two losses: traditional prediction and mutual information maximization. To address this variability, we introduce the \textit{sample reweighting} module to improve information utilization. This module assigns dual weights to each sample: one for prediction loss and another for mutual information loss, dynamically optimizing these weights via bi-level optimization. Our method achieves state-of-the-art or comparable performance across five mainstream TS tasks, including short-term and long-term forecasting, imputation, classification, and anomaly detection.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/e21eecfcf3e1b0d611d383f980313b51108d1e5a" target='_blank'>
-              A Temporal Linear Network for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/013fefa7c76d4f07cd5b40bb12553cb17be9e98c" target='_blank'>
+              LLM-TS Integrator: Integrating LLM for Enhanced Time Series Modeling
               </a>
             </td>
           <td>
-            Remi Genet, Hugo Inzirillo
+            Can Chen, Gabriel Oliveira, H. S. Noghabi, Tristan Sylvain
           </td>
-          <td>2024-10-28</td>
+          <td>2024-10-21</td>
           <td>ArXiv</td>
           <td>0</td>
           <td>2</td>
         </tr>
 
-        <tr id="Time series data are inherently functions of time, yet current transformers often learn time series by modeling them as mere concatenations of time periods, overlooking their functional properties. In this work, we propose a novel objective for transformers that learn time series by re-interpreting them as temporal functions. We build an alternative sequence of time series by constructing degradation operators of different intensity in the functional space, creating augmented variants of the original sample that are abstracted or simplified to different degrees. Based on the new set of generated sequence, we train an autoregressive transformer that progressively recovers the original sample from the most simplified variant. Analogous to the next word prediction task in languages that learns narratives by connecting different words, our autoregressive transformer aims to learn the Narratives of Time Series (NoTS) by connecting different functions in time. Theoretically, we justify the construction of the alternative sequence through its advantages in approximating functions. When learning time series data with transformers, constructing sequences of temporal functions allows for a broader class of approximable functions (e.g., differentiation) compared to sequences of time periods, leading to a 26\% performance improvement in synthetic feature regression experiments. Experimentally, we validate NoTS in 3 different tasks across 22 real-world datasets, where we show that NoTS significantly outperforms other pre-training methods by up to 6\%. Additionally, combining NoTS on top of existing transformer architectures can consistently boost the performance. Our results demonstrate the potential of NoTS as a general-purpose dynamic learner, offering a viable alternative for developing foundation models for time series analysis.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f8d3c810735743158f72e2f48101233f091cf165" target='_blank'>
-              Generalizable autoregressive modeling of time series through functional narratives
-              </a>
-            </td>
-          <td>
-            Ran Liu, Wenrui Ma, Ellen L. Zippi, Hadi Pouransari, Jingyun Xiao, Chris Sandino, Behrooz Mahasseni, Juri Minxha, Erdrin Azemi, Eva L. Dyer, Ali Moin
-          </td>
-          <td>2024-10-10</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>7</td>
-        </tr>
-
-        <tr id="High computational power and the availability of large datasets have supported the development of Foundational Models. They are a new emerging technique widely used in Generative Artificial Intelligence, characterized by their scalability and their use in Transfer Learning. The enormous and heterogeneous amounts of data used in their initial training phase, known as pre-training, give them a higher generalization capacity than any other specific model, constituting a solid base that can be adapted or adjusted to a wide range of tasks, increasing their applicability. This study proposes LLIAM, the Llama Lora-Integrated Autorregresive Model. Low-Rank Adaptations are used to enhance the knowledge of the model with diverse time series datasets, known as the fine-tuning phase. To illustrate the capabilities of our proposal, two sets of experiments have been carried out that obtained favorable and promising results with lower training times than other Deep Learning approaches. With this work, we also encourage the use of available resources (such as these pre-trained models) to avoid unnecessary and costly training, narrowing the gap between the goals of traditional Artificial Intelligence and those specified by the definition of Green Artificial Intelligence.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/f8758c3d5de62c1dfef81fcfcb74ae72fd369bbc" target='_blank'>
-              Transfer Learning with Foundational Models for Time Series Forecasting using Low-Rank Adaptations
-              </a>
-            </td>
-          <td>
-            M. Germ´an-Morales, A. J. Rivera-Rivas, M. J. del, Jesus D´ıaz, C. J. Carmona
-          </td>
-          <td>2024-10-15</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
-        <tr id="Due to the recurrent structure of RNN, the long information propagation path poses limitations in capturing long-term dependencies, gradient explosion/vanishing issues, and inefficient sequential execution. Based on this, we propose a novel paradigm called Parallel Gated Network (PGN) as the new successor to RNN. PGN directly captures information from previous time steps through the designed Historical Information Extraction (HIE) layer and leverages gated mechanisms to select and fuse it with the current time step information. This reduces the information propagation path to $\mathcal{O}(1)$, effectively addressing the limitations of RNN. To enhance PGN's performance in long-range time series forecasting tasks, we propose a novel temporal modeling framework called Temporal PGN (TPGN). TPGN incorporates two branches to comprehensively capture the semantic information of time series. One branch utilizes PGN to capture long-term periodic patterns while preserving their local characteristics. The other branch employs patches to capture short-term information and aggregate the global representation of the series. TPGN achieves a theoretical complexity of $\mathcal{O}(\sqrt{L})$, ensuring efficiency in its operations. Experimental results on five benchmark datasets demonstrate the state-of-the-art (SOTA) performance and high efficiency of TPGN, further confirming the effectiveness of PGN as the new successor to RNN in long-range time series forecasting. The code is available in this repository: \url{https://github.com/Water2sea/TPGN}.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/8239f7ea62fd748de80f4420719598ed87f74cc0" target='_blank'>
-              PGN: The RNN's New Successor is Effective for Long-Range Time Series Forecasting
-              </a>
-            </td>
-          <td>
-            Yuxin Jia, Youfang Lin, Jing Yu, Shuo Wang, Tianhao Liu, Huaiyu Wan
-          </td>
-          <td>2024-09-26</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>20</td>
-        </tr>
-
-        <tr id="Transformer-based architectures have achieved remarkable success in natural language processing and computer vision. However, their performance in multivariate long-term forecasting often lags behind simpler linear baselines. Previous studies have identified the traditional attention mechanism as a significant factor contributing to this limitation. To unlock the full potential of transformers for multivariate time series forecasting, I introduce \textbf{LSEAttention}, an approach designed to address entropy collapse and training instability commonly observed in transformer models. I validate the effectiveness of LSEAttention across various real-world multivariate time series datasets, demonstrating that it not only outperforms existing time series transformer models but also exceeds the performance of some state-of-the-art models on specific datasets.">
+        <tr id="Recent research has challenged the necessity of complex deep learning architectures for time series forecasting, demonstrating that simple linear models can often outperform sophisticated approaches. Building upon this insight, we introduce a novel architecture the Temporal Linear Net (TLN), that extends the capabilities of linear models while maintaining interpretability and computational efficiency. TLN is designed to effectively capture both temporal and feature-wise dependencies in multivariate time series data. Our approach is a variant of TSMixer that maintains strict linearity throughout its architecture. TSMixer removes activation functions, introduces specialized kernel initializations, and incorporates dilated convolutions to handle various time scales, while preserving the linear nature of the model. Unlike transformer-based models that may lose temporal information due to their permutation-invariant nature, TLN explicitly preserves and leverages the temporal structure of the input data. A key innovation of TLN is its ability to compute an equivalent linear model, offering a level of interpretability not found in more complex architectures such as TSMixer. This feature allows for seamless conversion between the full TLN model and its linear equivalent, facilitating both training flexibility and inference optimization.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/840ec4eb598b1a681385cbf594d5086843ef6694" target='_blank'>
-              LSEAttention is All You Need for Time Series Forecasting
+          <td><a href="https://www.semanticscholar.org/paper/e21eecfcf3e1b0d611d383f980313b51108d1e5a" target='_blank'>
+              A Temporal Linear Network for Time Series Forecasting
               </a>
             </td>
           <td>
-            Dizhen Liang
+            Remi Genet, Hugo Inzirillo
           </td>
-          <td>2024-10-31</td>
+          <td>2024-10-28</td>
           <td>ArXiv</td>
           <td>0</td>
-          <td>0</td>
+          <td>2</td>
         </tr>
 
     </tbody>
@@ -1552,7 +1552,7 @@ <h3 id="recommended_articles">Recommended articles on <i>Time-series forecasting
 
   var trace1 = {
     x: ['2024'],
-    y: [3],
+    y: [4],
     name: 'Num of citations',
     yaxis: 'y1',
     type: 'scatter'
diff --git a/site/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378/index.html b/site/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378/index.html
index 27e47055..7ed0f4c5 100644
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+++ b/site/recommendations/06a0ba437d41a7c82c08a9636a4438c1b5031378/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:05 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:45 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
diff --git a/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html b/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html
index 3bd74cdf..26d380e6 100644
--- a/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html
+++ b/site/recommendations/0acd117521ef5aafb09fed02ab415523b330b058/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:15 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:51 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -508,8 +508,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
 
         <tr id="None">
@@ -532,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
 
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2017-12-01</td>
           <td>2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)</td>
           <td>12</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html b/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html
index f8f2412d..85f2f0b4 100644
--- a/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html
+++ b/site/recommendations/0d01d21137a5af9f04e4b16a55a0f732cb8a540b/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:36 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:10 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-24</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>9</td>
           <td>50</td>
         </tr>
 
@@ -543,7 +543,7 @@ <h3 data-intro='Recommendations for the article'>
             Hongyuan Yu, Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, A. Liu
           </td>
           <td>2022-07-01</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>44</td>
           <td>35</td>
         </tr>
diff --git a/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html b/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html
index 6699443d..c7c39fca 100644
--- a/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html
+++ b/site/recommendations/123acfbccca0460171b6b06a4012dbb991cde55b/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:37 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:12 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -478,13 +478,13 @@ <h3 data-intro='Recommendations for the article'>
 
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
           <td>
             Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>69</td>
           <td>18</td>
         </tr>
 
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-06-22</td>
           <td>ArXiv</td>
-          <td>11</td>
+          <td>13</td>
           <td>3</td>
         </tr>
 
@@ -531,8 +531,8 @@ <h3 data-intro='Recommendations for the article'>
             Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long
           </td>
           <td>2024-02-04</td>
-          <td>DBLP, ArXiv</td>
-          <td>18</td>
+          <td>ArXiv, DBLP</td>
+          <td>21</td>
           <td>67</td>
         </tr>
 
@@ -544,7 +544,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-05</td>
           <td>ArXiv</td>
-          <td>57</td>
+          <td>60</td>
           <td>5</td>
         </tr>
 
@@ -568,7 +568,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-16</td>
           <td>ArXiv</td>
-          <td>4</td>
+          <td>5</td>
           <td>8</td>
         </tr>
 
@@ -580,7 +580,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>184</td>
+          <td>195</td>
           <td>9</td>
         </tr>
 
diff --git a/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html b/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html
index cca0614e..c7fb2726 100644
--- a/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html
+++ b/site/recommendations/16f01c1b3ddd0b2abd5ddfe4fdb3f74767607277/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:41 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:18 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -496,7 +496,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-16</td>
           <td>ArXiv</td>
-          <td>4</td>
+          <td>5</td>
           <td>8</td>
         </tr>
 
@@ -508,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-25</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>11</td>
           <td>8</td>
         </tr>
 
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-09-20</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>77</td>
+          <td>80</td>
           <td>17</td>
         </tr>
 
@@ -568,7 +568,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-08-16</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>22</td>
           <td>3</td>
         </tr>
 
@@ -580,7 +580,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-09-17</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>1</td>
         </tr>
 
diff --git a/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html b/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html
index 7774fa84..380005ad 100644
--- a/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html
+++ b/site/recommendations/1df04f33a8ef313cc2067147dbb79c3ca7c5c99f/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:38 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:13 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -495,8 +495,8 @@ <h3 data-intro='Recommendations for the article'>
             Ali Behrouz, Farnoosh Hashemi
           </td>
           <td>2024-02-13</td>
-          <td>DBLP, ArXiv</td>
-          <td>37</td>
+          <td>ArXiv, DBLP</td>
+          <td>39</td>
           <td>9</td>
         </tr>
 
diff --git a/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html b/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html
index 95344014..538a404e 100644
--- a/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html
+++ b/site/recommendations/279cd637b7e38bba1dd8915b5ce68cbcacecbe68/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:46 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:28 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -483,7 +483,7 @@ <h3 data-intro='Recommendations for the article'>
             Andreas Doerr, Christian Daniel, Martin Schiegg, D. Nguyen-Tuong, S. Schaal, Marc Toussaint, Sebastian Trimpe
           </td>
           <td>2018-01-31</td>
-          <td>DBLP, MAG, ArXiv</td>
+          <td>ArXiv, MAG, DBLP</td>
           <td>113</td>
           <td>93</td>
         </tr>
@@ -500,6 +500,18 @@ <h3 data-intro='Recommendations for the article'>
           <td>40</td>
         </tr>
 
+        <tr id="World modelling is essential for understanding and predicting the dynamics of complex systems by learning both spatial and temporal dependencies. However, current frameworks, such as Transformers and selective state-space models like Mambas, exhibit limitations in efficiently encoding spatial and temporal structures, particularly in scenarios requiring long-term high-dimensional sequence modelling. To address these issues, we propose a novel recurrent framework, the \textbf{FACT}ored \textbf{S}tate-space (\textbf{FACTS}) model, for spatial-temporal world modelling. The FACTS framework constructs a graph-structured memory with a routing mechanism that learns permutable memory representations, ensuring invariance to input permutations while adapting through selective state-space propagation. Furthermore, FACTS supports parallel computation of high-dimensional sequences. We empirically evaluate FACTS across diverse tasks, including multivariate time series forecasting and object-centric world modelling, demonstrating that it consistently outperforms or matches specialised state-of-the-art models, despite its general-purpose world modelling design.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/ec840755867d3f5cf175cb57de963f042297f4ef" target='_blank'>FACTS: A Factored State-Space Framework For World Modelling</a></td>
+          <td>
+            Li Nanbo, Firas Laakom, Yucheng Xu, Wenyi Wang, Jurgen Schmidhuber
+          </td>
+          <td>2024-10-28</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>7</td>
+        </tr>
+
         <tr id="Time series with long-term structure arise in a variety of contexts and capturing this temporal structure is a critical challenge in time series analysis for both inference and forecasting settings. Traditionally, state space models have been successful in providing uncertainty estimates of trajectories in the latent space. More recently, deep learning, attention-based approaches have achieved state of the art performance for sequence modeling, though often require large amounts of data and parameters to do so. We propose Stanza, a nonlinear, non-stationary state space model as an intermediate approach to fill the gap between traditional models and modern deep learning approaches for complex time series. Stanza strikes a balance between competitive forecasting accuracy and probabilistic, interpretable inference for highly structured time series. In particular, Stanza achieves forecasting accuracy competitive with deep LSTMs on real-world datasets, especially for multi-step ahead forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/9c619e58d073772ff298228d47afcab625c7f37a" target='_blank'>Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series</a></td>
@@ -524,6 +536,18 @@ <h3 data-intro='Recommendations for the article'>
           <td>50</td>
         </tr>
 
+        <tr id="Forecasting the behaviour of complex dynamical systems such as interconnected sensor networks characterized by high-dimensional multivariate time series(MTS) is of paramount importance for making informed decisions and planning for the future in a broad spectrum of applications. Graph forecasting networks(GFNs) are well-suited for forecasting MTS data that exhibit spatio-temporal dependencies. However, most prior works of GFN-based methods on MTS forecasting rely on domain-expertise to model the nonlinear dynamics of the system, but neglect the potential to leverage the inherent relational-structural dependencies among time series variables underlying MTS data. On the other hand, contemporary works attempt to infer the relational structure of the complex dependencies between the variables and simultaneously learn the nonlinear dynamics of the interconnected system but neglect the possibility of incorporating domain-specific prior knowledge to improve forecast accuracy. To this end, we propose a hybrid architecture that combines explicit prior knowledge with implicit knowledge of the relational structure within the MTS data. It jointly learns intra-series temporal dependencies and inter-series spatial dependencies by encoding time-conditioned structural spatio-temporal inductive biases to provide more accurate and reliable forecasts. It also models the time-varying uncertainty of the multi-horizon forecasts to support decision-making by providing estimates of prediction uncertainty. The proposed architecture has shown promising results on multiple benchmark datasets and outperforms state-of-the-art forecasting methods by a significant margin. We report and discuss the ablation studies to validate our forecasting architecture.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/05cd3afc8208f1c0dd61b09a90f35dd42497e175" target='_blank'>Multi-Knowledge Fusion Network for Time Series Representation Learning</a></td>
+          <td>
+            Sakhinana Sagar Srinivas, Shivam Gupta, Krishna Sai Sudhir Aripirala, Venkataramana Runkana
+          </td>
+          <td>2024-08-22</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>3</td>
+        </tr>
+
         <tr id="Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time series datasets.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/7a1e5377b08489c2969f73c56efc557e34f578e1" target='_blank'>Relational State-Space Model for Stochastic Multi-Object Systems</a></td>
@@ -548,34 +572,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>12</td>
         </tr>
 
-        <tr id="Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep learning layers for efficient sequence modeling. However, we find fundamental limitations with these prior approaches, proving their SSM representations cannot express autoregressive time series processes. We thus introduce SpaceTime, a new state-space time series architecture that improves all three criteria. For expressivity, we propose a new SSM parameterization based on the companion matrix -- a canonical representation for discrete-time processes -- which enables SpaceTime's SSM layers to learn desirable autoregressive processes. For long horizon forecasting, we introduce a"closed-loop"variation of the companion SSM, which enables SpaceTime to predict many future time-steps by generating its own layer-wise inputs. For efficient training and inference, we introduce an algorithm that reduces the memory and compute of a forward pass with the companion matrix. With sequence length $\ell$ and state-space size $d$, we go from $\tilde{O}(d \ell)$ na\"ively to $\tilde{O}(d + \ell)$. In experiments, our contributions lead to state-of-the-art results on extensive and diverse benchmarks, with best or second-best AUROC on 6 / 7 ECG and speech time series classification, and best MSE on 14 / 16 Informer forecasting tasks. Furthermore, we find SpaceTime (1) fits AR($p$) processes that prior deep SSMs fail on, (2) forecasts notably more accurately on longer horizons than prior state-of-the-art, and (3) speeds up training on real-world ETTh1 data by 73% and 80% relative wall-clock time over Transformers and LSTMs.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/a7d68b1702af08ce4dbbf2cd0b083e744ae5c6be" target='_blank'>Effectively Modeling Time Series with Simple Discrete State Spaces</a></td>
-          <td>
-            Michael Zhang, Khaled Kamal Saab, Michael Poli, Tri Dao, Karan Goel, Christopher Ré
-          </td>
-          <td>2023-03-16</td>
-          <td>ArXiv</td>
-          <td>31</td>
-          <td>45</td>
-        </tr>
-
-        <tr id="
-
- Gaussian state space models have been used for decades as generative models of sequential data. They admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy widespread adoption. We introduce a unified algorithm to efficiently learn a broad class of linear and non-linear state space models, including variants where the emission and transition distributions are modeled by deep neural networks. Our learning algorithm simultaneously learns a compiled inference network and the generative model, leveraging a structured variational approximation parameterized by recurrent neural networks to mimic the posterior distribution. We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood.
-
-">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/2af17f153e3fd71e15db9216b972aef222f46617" target='_blank'>Structured Inference Networks for Nonlinear State Space Models</a></td>
-          <td>
-            R. G. Krishnan, Uri Shalit, D. Sontag
-          </td>
-          <td>2016-09-30</td>
-          <td>DBLP, MAG, ArXiv</td>
-          <td>435</td>
-          <td>48</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html b/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html
index 1bef06ad..fcd4156d 100644
--- a/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html
+++ b/site/recommendations/2b2aa13d4959073f61ad70555bc8c7da7d116196/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:10 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:47 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -556,10 +556,22 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-07-02</td>
           <td>ArXiv</td>
-          <td>134</td>
+          <td>136</td>
           <td>23</td>
         </tr>
 
+        <tr id="None">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/de3567866929da3a25754f93f917101717008833" target='_blank'>Nonlinear system identification with regularized Tensor Network B-splines</a></td>
+          <td>
+            Ridvan Karagoz, Kim Batselier
+          </td>
+          <td>2020-03-17</td>
+          <td>Autom.</td>
+          <td>18</td>
+          <td>4</td>
+        </tr>
+
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/a9a3238808d20c5236a7c5f912c20e6dd729f88c" target='_blank'>Data-driven Reconstruction of Nonlinear Dynamics from Sparse Observation</a></td>
@@ -584,18 +596,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>33</td>
         </tr>
 
-        <tr id="This article discusses a newly developed online manifold learning method, subspace iteration using reduced models (SIRM), for the dimensionality reduction of dynamical systems. This method may be viewed as subspace iteration combined with a model reduction procedure. Specifically, starting with a test solution, the method solves a reduced model to obtain a more precise solution, and it repeats this process until sufficient accuracy is achieved. The reduced model is obtained by projecting the full model onto a subspace that is spanned by the dominant modes of an extended data ensemble. The extended data ensemble in this article contains not only the state vectors of some snapshots of the approximate solution from the previous iteration but also the associated tangent vectors. Therefore, the proposed manifold learning method takes advantage of the information of the original dynamical system to reduce the dynamics. Moreover, the learning procedure is computed in the online stage, as opposed to being compute...">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/418ebf87dea83c922735e9083fe533efc8a04b7f" target='_blank'>An Online Manifold Learning Approach for Model Reduction of Dynamical Systems</a></td>
-          <td>
-            Liqian Peng, K. Mohseni
-          </td>
-          <td>2012-10-10</td>
-          <td>SIAM J. Numer. Anal.</td>
-          <td>22</td>
-          <td>40</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html b/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html
index f8a194de..18f35bea 100644
--- a/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html
+++ b/site/recommendations/35e2571c17246577e0bc1b9de57a314c3b60e220/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:14 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:50 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,8 +484,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2019-06-25</td>
           <td>IEEE Access</td>
-          <td>104</td>
-          <td>66</td>
+          <td>103</td>
+          <td>67</td>
         </tr>
 
         <tr id="Energy conservation is a basic physics principle, the breakdown of which often implies new physics. This paper presents a method for data-driven "new physics" discovery. Specifically, given a trajectory governed by unknown forces, our neural new-physics detector (NNPhD) aims to detect new physics by decomposing the force field into conservative and nonconservative components, which are represented by a Lagrangian neural network (LNN) and an unconstrained neural network, respectively, trained to minimize the force recovery error plus a constant λ times the magnitude of the predicted nonconservative force. We show that a phase transition occurs at λ=1, universally for arbitrary forces. We demonstrate that NNPhD successfully discovers new physics in toy numerical experiments, rediscovering friction (1493) from a damped double pendulum, Neptune from Uranus' orbit (1846), and gravitational waves (2017) from an inspiraling orbit. We also show how NNPhD coupled with an integrator outperforms both an LNN and an unconstrained neural network for predicting the future of a damped double pendulum.">
@@ -543,7 +543,7 @@ <h3 data-intro='Recommendations for the article'>
             Philipp Holl, V. Koltun, Nils Thuerey
           </td>
           <td>2021-09-30</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>5</td>
           <td>105</td>
         </tr>
@@ -568,7 +568,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-05-06</td>
           <td>Physical review letters</td>
-          <td>92</td>
+          <td>93</td>
           <td>82</td>
         </tr>
 
diff --git a/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html b/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html
index 81b1d1b4..5dc3e7e9 100644
--- a/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html
+++ b/site/recommendations/3c9961153493370500020c81527b3548c96f81e0/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:02 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:42 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,8 +484,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
 
         <tr id="None">
@@ -496,7 +496,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
 
@@ -532,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-11-19</td>
           <td>Proceedings of the Royal Society A</td>
-          <td>12</td>
+          <td>13</td>
           <td>32</td>
         </tr>
 
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-07-22</td>
           <td>Communications Physics</td>
           <td>20</td>
-          <td>94</td>
+          <td>95</td>
         </tr>
 
         <tr id="Estimating governing equations from observed time-series data is crucial for understanding dynamical systems. From the perspective of system comprehension, the demand for accurate estimation and interpretable results has been particularly emphasized. Herein, we propose a novel data-driven method for estimating the governing equations of dynamical systems based on machine learning with high accuracy and interpretability. The proposed method enhances the estimation accuracy for dynamical systems using sparse modeling by incorporating physical constraints derived from Hamiltonian mechanics. Unlike conventional approaches used for estimating governing equations for dynamical systems, we employ a sparse representation of Hamiltonian, allowing for the estimation. Using noisy observational data, the proposed method demonstrates a capability to achieve accurate parameter estimation and extraction of essential nonlinear terms. In addition, it is shown that estimations based on energy conservation principles exhibit superior accuracy in long-term predictions. These results collectively indicate that the proposed method accurately estimates dynamical systems while maintaining interpretability.">
diff --git a/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html b/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html
index f28e085f..10178080 100644
--- a/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html
+++ b/site/recommendations/3df50e9b73cc2937dfd651f4c3344bc99b7ed3f2/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:03 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:42 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -476,6 +476,18 @@ <h3 data-intro='Recommendations for the article'>
   </thead>
   <tbody>
 
+        <tr id="This article introduces an advanced Koopman mode decomposition (KMD) technique—coined Featurized Koopman Mode Decomposition (FKMD)—that uses delay embedding and a learned Mahalanobis distance to enhance analysis and prediction of high-dimensional dynamical systems. The delay embedding expands the observation space to better capture underlying manifold structures, while the Mahalanobis distance adjusts observations based on the system’s dynamics. This aids in featurizing KMD in cases where good features are not a priori known. We show that FKMD improves predictions for a high-dimensional linear oscillator, a high-dimensional Lorenz attractor that is partially observed, and a cell signaling problem from cancer research.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/78f159b92f2f6689b738970dc1ce3a8641b2a534" target='_blank'>Featurizing Koopman mode decomposition for robust forecasting</a></td>
+          <td>
+            D. Aristoff, J. Copperman, Nathan Mankovich, Alexander Davies
+          </td>
+          <td>2023-12-14</td>
+          <td>The Journal of Chemical Physics</td>
+          <td>0</td>
+          <td>12</td>
+        </tr>
+
         <tr id="A common problem in time-series analysis is to predict dynamics with only scalar or partial observations of the underlying dynamical system. For data on a smooth compact manifold, Takens' theorem proves a time-delayed embedding of the partial state is diffeomorphic to the attractor, although for chaotic and highly nonlinear systems, learning these delay coordinate mappings is challenging. We utilize deep artificial neural networks (ANNs) to learn discrete time maps and continuous time flows of the partial state. Given training data for the full state, we also learn a reconstruction map. Thus, predictions of a time series can be made from the current state and several previous observations with embedding parameters determined from time-series analysis. The state space for time evolution is of comparable dimension to reduced order manifold models. These are advantages over recurrent neural network models, which require a high-dimensional internal state or additional memory terms and hyperparameters. We demonstrate the capacity of deep ANNs to predict chaotic behavior from a scalar observation on a manifold of dimension three via the Lorenz system. We also consider multivariate observations on the Kuramoto-Sivashinsky equation, where the observation dimension required for accurately reproducing dynamics increases with the manifold dimension via the spatial extent of the system.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/e001a698302d507abbd3a818e0b0aaf84f02e418" target='_blank'>Deep learning delay coordinate dynamics for chaotic attractors from partial observable data</a></td>
@@ -496,8 +508,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-01-13</td>
           <td>Proceedings of the Royal Society A</td>
-          <td>73</td>
-          <td>66</td>
+          <td>75</td>
+          <td>67</td>
         </tr>
 
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -509,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>82</td>
+          <td>83</td>
         </tr>
 
         <tr id="Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.">
@@ -521,7 +533,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2020-05-16</td>
           <td>Chaos</td>
           <td>1</td>
-          <td>82</td>
+          <td>83</td>
         </tr>
 
         <tr id="Many complex phenomena, from weather systems to heartbeat rhythm patterns, are effectively modeled as low-dimensional dynamical systems. Such systems may behave chaotically under certain conditions, and so the ability to detect chaos based on empirical measurement is an important step in characterizing and predicting these processes. Classifying a system as chaotic usually requires estimating its largest Lyapunov exponent, which quantifies the average rate of convergence or divergence of initially close trajectories in state space, and for which a positive value is generally accepted as an operational definition of chaos. Estimating the largest Lyapunov exponent from observations of a process is especially challenging in systems affected by dynamical noise, which is the case for many models of real-world processes, in particular models of biological systems. We describe a novel method for estimating the largest Lyapunov exponent from data, based on training Deep Learning models on synthetically generated trajectories, and demonstrate that this method yields accurate and noise-robust predictions given relatively short inputs and across a range of different dynamical systems. Our method is unique in that it can analyze tree-shaped data, a ubiquitous topology in biological settings, and specifically in dynamics over lineages of cells or organisms. We also characterize the types of input information extracted by our models for their predictions, allowing for a deeper understanding into the different ways by which chaos can be analyzed in different topologies.">
@@ -544,7 +556,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-06-01</td>
           <td>Science Advances</td>
-          <td>49</td>
+          <td>50</td>
           <td>50</td>
         </tr>
 
@@ -560,30 +572,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>50</td>
         </tr>
 
-        <tr id="Complex systems are commonly modeled using nonlinear dynamical systems. These models are often high-dimensional and chaotic. An important goal in studying physical systems through the lens of mathematical models is to determine when the system undergoes changes in qualitative behavior. A detailed description of the dynamics can be difficult or impossible to obtain for high-dimensional and chaotic systems. Therefore, a more sensible goal is to recognize and mark transitions of a system between qualitatively different regimes of behavior. In practice, one is interested in developing techniques for detection of such transitions from sparse observations, possibly contaminated by noise. In this paper we develop a framework to accurately tag different regimes of complex systems based on topological features. In particular, our framework works with a high degree of success in picking out a cyclically orbiting regime from a stationary equilibrium regime in high-dimensional stochastic dynamical systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4201cd896bbab7c98602be04bb744b6c470b0dfb" target='_blank'>Automatic recognition and tagging of topologically different regimes in dynamical systems</a></td>
-          <td>
-            Jesse Berwald, M. Gidea, Mikael Vejdemo-Johansson
-          </td>
-          <td>2013-12-09</td>
-          <td>ArXiv</td>
-          <td>35</td>
-          <td>22</td>
-        </tr>
-
-        <tr id="None">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/0515c6b8af04ab03d06dcbb61caf851ff899fc37" target='_blank'>Dynamics reconstruction and classification via Koopman features</a></td>
-          <td>
-            Wei Zhang, Yao-Chi Yu, Jr-Shin Li
-          </td>
-          <td>2019-06-24</td>
-          <td>Data Mining and Knowledge Discovery</td>
-          <td>3</td>
-          <td>28</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html b/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html
index 020d6cd3..34889c39 100644
--- a/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html
+++ b/site/recommendations/455bfc515eb279cc09023faa1f78c6efb61224ba/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:34 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:08 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-03-01</td>
           <td>ArXiv</td>
-          <td>16</td>
+          <td>17</td>
           <td>65</td>
         </tr>
 
@@ -532,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-03-01</td>
           <td>ArXiv</td>
-          <td>16</td>
+          <td>17</td>
           <td>65</td>
         </tr>
 
@@ -567,9 +567,9 @@ <h3 data-intro='Recommendations for the article'>
             Steven Cheng-Xian Li, Benjamin M Marlin
           </td>
           <td>2020-07-12</td>
-          <td>DBLP, MAG, ArXiv</td>
-          <td>53</td>
-          <td>33</td>
+          <td>ArXiv, MAG, DBLP</td>
+          <td>54</td>
+          <td>34</td>
         </tr>
 
         <tr id="We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, or activity recognition tasks over different individuals from multiple wearable sensors. We focus on two under-explored practical challenges arising in such settings: (i) Each task may have a different subset of sensors, i.e., providing different partial observations of the underlying ‘system’. This restriction can be due to different manufacturers in the former case, and people wearing more or less measurement devices in the latter (ii) We are not allowed to store or re-access data from a task once it has been observed at the task level. This may be due to privacy considerations in the case of people, or legal restrictions placed by machine owners. Nevertheless, we would like to (a) improve performance on subsequent tasks using experience from completed tasks as well as (b) continue to perform better on past tasks, e.g., update the model and improve predictions on even the first machine after learning from subsequently observed ones. We note that existing continual learning methods do not take into account variability in input dimensions arising due to different subsets of sensors being available across tasks, and struggle to adapt to such variable input dimensions (VID) tasks. In this work, we address this shortcoming of existing methods. To this end, we learn task-specific generative models and classifiers, and use these to augment data for target tasks. Since the input dimensions across tasks vary, we propose a novel conditioning module based on graph neural networks to aid a standard recurrent neural network. We evaluate the efficacy of the proposed approach on three publicly available datasets corresponding to two activity recognition tasks (classification) and one prognostics task (regression). We demonstrate that it is possible to significantly enhance the performance on future and previous tasks while learning continuously from VID tasks without storing data.">
@@ -581,7 +581,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-12-01</td>
           <td>2021 IEEE International Conference on Data Mining (ICDM)</td>
           <td>17</td>
-          <td>27</td>
+          <td>28</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html b/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html
index a220a30d..a13c9df8 100644
--- a/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html
+++ b/site/recommendations/4971f9abd024e40fbbdff2e9492745b68a6bca01/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:08 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:46 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -508,8 +508,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>116</td>
-          <td>66</td>
+          <td>119</td>
+          <td>67</td>
         </tr>
 
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
diff --git a/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html b/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html
index f658667f..227bb1fb 100644
--- a/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html
+++ b/site/recommendations/4a111f7a3b56d0468f13104999844885157ef17d/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:40 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:17 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -508,19 +508,19 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-09-24</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>9</td>
         </tr>
 
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
           <td>
             Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>69</td>
           <td>18</td>
         </tr>
 
@@ -531,8 +531,8 @@ <h3 data-intro='Recommendations for the article'>
             Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long
           </td>
           <td>2024-02-04</td>
-          <td>DBLP, ArXiv</td>
-          <td>18</td>
+          <td>ArXiv, DBLP</td>
+          <td>21</td>
           <td>67</td>
         </tr>
 
@@ -544,10 +544,22 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-11-27</td>
           <td>ArXiv</td>
-          <td>701</td>
+          <td>724</td>
           <td>35</td>
         </tr>
 
+        <tr id="Developing a foundation model for time series forecasting across diverse domains has attracted significant attention in recent years. Existing works typically assume regularly sampled, well-structured data, limiting their applicability to more generalized scenarios where time series often contain missing values, unequal sequence lengths, and irregular time intervals between measurements. To cover diverse domains and handle variable regularities, we propose FlexTSF, a universal time series forecasting model that possesses better generalization and natively support both regular and irregular time series. FlexTSF produces forecasts in an autoregressive manner and incorporates three novel designs: VT-Norm, a normalization strategy to ablate data domain barriers, IVP Patcher, a patching module to learn representations from flexibly structured time series, and LED attention, an attention mechanism to seamlessly integrate these two and propagate forecasts with awareness of domain and time information. Experiments on 12 datasets show that FlexTSF outperforms state-of-the-art forecasting models respectively designed for regular and irregular time series. Furthermore, after self-supervised pre-training, FlexTSF shows exceptional performance in both zero-shot and few-show settings for time series forecasting.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/45521591fd8c46068f0a8712b43ce6cdaac1edea" target='_blank'>FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities</a></td>
+          <td>
+            Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl, Simon Gottschalk
+          </td>
+          <td>2024-10-30</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>6</td>
+        </tr>
+
         <tr id="Time series forecasting is a critical and challenging task in practical application. Recent advancements in pre-trained foundation models for time series forecasting have gained significant interest. However, current methods often overlook the multi-scale nature of time series, which is essential for accurate forecasting. To address this, we propose HiMTM, a hierarchical multi-scale masked time series modeling with self-distillation for long-term forecasting. HiMTM integrates four key components: (1) hierarchical multi-scale transformer (HMT) to capture temporal information at different scales; (2) decoupled encoder-decoder (DED) that directs the encoder towards feature extraction while the decoder focuses on pretext tasks; (3) hierarchical self-distillation (HSD) for multi-stage feature-level supervision signals during pre-training; and (4) cross-scale attention fine-tuning (CSA-FT) to capture dependencies between different scales for downstream tasks. These components collectively enhance multi-scale feature extraction in masked time series modeling, improving forecasting accuracy. Extensive experiments on seven mainstream datasets show that HiMTM surpasses state-of-the-art self-supervised and end-to-end learning methods by a considerable margin of 3.16-68.54\%. Additionally, HiMTM outperforms the latest robust self-supervised learning method, PatchTST, in cross-domain forecasting by a significant margin of 2.3\%. The effectiveness of HiMTM is further demonstrated through its application in natural gas demand forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/d945eae22457648e191742c37bcad88a114ce703" target='_blank'>HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting</a></td>
@@ -560,18 +572,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>7</td>
         </tr>
 
-        <tr id="The Transformer is a highly successful deep learning model that has revolutionised the world of artificial neural networks, first in natural language processing and later in computer vision. This model is based on the attention mechanism and is able to capture complex semantic relationships between a variety of patterns present in the input data. Precisely because of these characteristics, the Transformer has recently been exploited for time series forecasting problems, assuming a natural adaptability to the domain of continuous numerical series. Despite the acclaimed results in the literature, some works have raised doubts about the robustness and effectiveness of this approach. In this paper, we further investigate the effectiveness of Transformer-based models applied to the domain of time series forecasting, demonstrate their limitations, and propose a set of alternative models that are better performing and significantly less complex. In particular, we empirically show how simplifying Transformer-based forecasting models almost always leads to an improvement, reaching state of the art performance. We also propose shallow models without the attention mechanism, which compete with the overall state of the art in long time series forecasting, and demonstrate their ability to accurately predict time series over extremely long windows. From a methodological perspective, we show how it is always necessary to use a simple baseline to verify the effectiveness of proposed models, and finally, we conclude the paper with a reflection on recent research paths and the opportunity to follow trends and hypes even where it may not be necessary.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/b98bc7d48357932537fc9ba6035e3b6bd1018ae6" target='_blank'>Two Steps Forward and One Behind: Rethinking Time Series Forecasting with Deep Learning</a></td>
-          <td>
-            Riccardo Ughi, Eugenio Lomurno, Matteo Matteucci
-          </td>
-          <td>2023-04-10</td>
-          <td>DBLP, ArXiv</td>
-          <td>1</td>
-          <td>6</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html b/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html
index 3ac9c2d8..822bd49a 100644
--- a/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html
+++ b/site/recommendations/5bd2c0acaf58c25f71617db2396188c74d29bf14/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:43 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:23 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -555,7 +555,7 @@ <h3 data-intro='Recommendations for the article'>
             Xiaoyong Jin, Youngsuk Park, Danielle C. Maddix, Bernie Wang, Xifeng Yan
           </td>
           <td>2021-02-13</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>63</td>
           <td>65</td>
         </tr>
diff --git a/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html b/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html
index b6394065..acacbe12 100644
--- a/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html
+++ b/site/recommendations/5d150cec2775f9bc863760448f14104cc8f42368/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:00 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:41 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,8 +484,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2019-03-29</td>
           <td>Proceedings of the National Academy of Sciences of the United States of America</td>
-          <td>645</td>
-          <td>66</td>
+          <td>648</td>
+          <td>67</td>
         </tr>
 
         <tr id="None">
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
 
@@ -581,7 +581,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2014-12-10</td>
           <td>The European Physical Journal Special Topics</td>
           <td>64</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
         <tr id="None">
@@ -593,7 +593,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2014-12-01</td>
           <td>The European Physical Journal Special Topics</td>
           <td>7</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html b/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html
index 522651d7..fae84787 100644
--- a/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html
+++ b/site/recommendations/60d0d998fa038182b3b69a57adb9b2f82d40589c/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:01 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:41 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-07-01</td>
           <td>Nature Computational Science</td>
-          <td>76</td>
+          <td>77</td>
           <td>21</td>
         </tr>
 
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-05-07</td>
           <td>ArXiv</td>
           <td>1</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
         <tr id="None">
@@ -521,7 +521,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2021-07-22</td>
           <td>Communications Physics</td>
           <td>20</td>
-          <td>94</td>
+          <td>95</td>
         </tr>
 
         <tr id="The extraction of models from data (in a sense, the “understanding” of the physical laws giving rise to the data) is a fundamental cognitive as well as scientific challenge. We show a geometric/analytic learning algorithm capable of creating minimal descriptions of parametrically dependent unknown nonlinear dynamical systems. This is accomplished by the data-driven discovery of useful intrinsic-state variables and parameters in terms of which one can empirically model the underlying dynamics. We discuss an informed observation geometry that enables us to formulate models without first principles as well as without closed form equations. (See pp. E7865–E7874.)">
@@ -548,18 +548,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>73</td>
         </tr>
 
-        <tr id="High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/ecf61b26615fc12f9edc4826dd81a23ac0d72f37" target='_blank'>Physics-informed deep-learning applications to experimental fluid mechanics</a></td>
-          <td>
-            Hamidreza Eivazi, Yuning Wang, R. Vinuesa
-          </td>
-          <td>2022-03-29</td>
-          <td>Measurement Science and Technology</td>
-          <td>25</td>
-          <td>39</td>
-        </tr>
-
         <tr id="None">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/606824bebf4e444fca9b988823883f1ec99f19ec" target='_blank'>A physics-informed operator regression framework for extracting data-driven continuum models</a></td>
@@ -584,6 +572,19 @@ <h3 data-intro='Recommendations for the article'>
           <td>47</td>
         </tr>
 
+        <tr id="
+ In the context of science, the well-known adage “a picture is worth a thousand words” might well be “a model is worth a thousand datasets.” Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reconciling data that is at odds with simplified models without requiring "big data". In this work demonstrate how a mathematical object, which we denote universal differential equations (UDEs), can be utilized as a theoretical underpinning to a diverse array of problems in scientific machine learning to yield efficient algorithms and generalized approaches. The UDE model augments scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating the training of physics-informed neural networks and large-eddy simulations, can all be transformed into UDE training problems that are efficiently solved by a single software methodology.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/696b388ee6221c6dbcfd647a06883b2bfee773d9" target='_blank'>Universal Differential Equations for Scientific Machine Learning</a></td>
+          <td>
+            Christopher Rackauckas, Yingbo Ma, Julius Martensen, Collin Warner, K. Zubov, R. Supekar, Dominic J. Skinner, A. Ramadhan
+          </td>
+          <td>2020-01-13</td>
+          <td>ArXiv</td>
+          <td>509</td>
+          <td>26</td>
+        </tr>
+
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html b/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html
index c1903312..7fbd134a 100644
--- a/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html
+++ b/site/recommendations/648d90b713997a771e2c49f02cd771e8b7b10b37/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:42 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:21 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-04-23</td>
           <td>Sensors (Basel, Switzerland)</td>
-          <td>27</td>
+          <td>28</td>
           <td>77</td>
         </tr>
 
@@ -591,7 +591,7 @@ <h3 data-intro='Recommendations for the article'>
             Konstantinos Kallidromitis, Denis A. Gudovskiy, Kozuka Kazuki, Ohama Iku, Luca Rigazio
           </td>
           <td>2021-10-24</td>
-          <td>DBLP, ArXiv</td>
+          <td>ArXiv, DBLP</td>
           <td>10</td>
           <td>13</td>
         </tr>
diff --git a/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html b/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html
index 8e168336..0f547d6d 100644
--- a/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html
+++ b/site/recommendations/73dd9c49f205280991826b2ea4b50344203916b4/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:11 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:49 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -544,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2017-11-15</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>455</td>
-          <td>66</td>
+          <td>461</td>
+          <td>67</td>
         </tr>
 
         <tr id="None">
@@ -568,8 +568,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>220</td>
-          <td>66</td>
+          <td>221</td>
+          <td>67</td>
         </tr>
 
         <tr id="We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system. We use a weighted least squares approach, and provide finite sample error bounds of the learned model as a function of the number of samples and various system parameters from the two systems as well as the weight assigned to the auxiliary data. We show that the auxiliary data can help to reduce the intrinsic system identification error due to noise, at the price of adding a portion of error that is due to the differences between the two system models. We further provide a data-dependent bound that is computable when some prior knowledge about the systems, such as upper bounds on noise levels and model difference, is available. This bound can also be used to determine the weight that should be assigned to the auxiliary data during the model training stage.">
@@ -581,7 +581,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-02-08</td>
           <td>ArXiv</td>
           <td>7</td>
-          <td>36</td>
+          <td>37</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html b/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html
index ef8304b1..3c6f1ceb 100644
--- a/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html
+++ b/site/recommendations/883547fdbd88552328a6615ec620f96e39c57018/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:19 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:51 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-07-05</td>
           <td>Royal Society Open Science</td>
-          <td>59</td>
+          <td>61</td>
           <td>32</td>
         </tr>
 
@@ -497,7 +497,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2023-01-30</td>
           <td>ArXiv</td>
           <td>11</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
         <tr id="None">
@@ -520,8 +520,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>220</td>
-          <td>66</td>
+          <td>221</td>
+          <td>67</td>
         </tr>
 
         <tr id="We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data. We recast the SINDy method within a Bayesian framework and use Gaussian approximations for the prior and likelihood to speed up computation. The resulting method, Bayesian-SINDy, not only quantifies uncertainty in the parameters estimated but also is more robust when learning the correct model from limited and noisy data. Using both synthetic and real-life examples such as Lynx-Hare population dynamics, we demonstrate the effectiveness of the new framework in learning correct model equations and compare its computational and data efficiency with existing methods. Because Bayesian-SINDy can quickly assimilate data and is robust against noise, it is particularly suitable for biological data and real-time system identification in control. Its probabilistic framework also enables the calculation of information entropy, laying the foundation for an active learning strategy.">
@@ -544,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
 
         <tr id="The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. However, SINDy assumes the user has prior knowledge of the variables in the system and of a function library that can act as a basis for the system. In this paper, we demonstrate on real world data how the Augmented SINDy algorithm outperforms SINDy in the presence of system variable uncertainty. We then show SINDy can be further augmented to perform robustly when both kinds of uncertainty are present.">
diff --git a/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html b/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html
index 7a80178b..710caf79 100644
--- a/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html
+++ b/site/recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:04 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:43 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -476,18 +476,6 @@ <h3 data-intro='Recommendations for the article'>
   </thead>
   <tbody>
 
-        <tr id="Many dynamical systems of interest are nonlinear, with examples in turbulence, epidemiology, neuroscience, and finance, making them difficult to control using linear approaches. Model predictive control (MPC) is a powerful model-based optimization technique that enables the control of such nonlinear systems with constraints. However, modern systems often lack computationally tractable models, motivating the use of system identification techniques to learn accurate and efficient models for real-time control. In this tutorial article, we review emerging data-driven methods for model discovery and how they are used for nonlinear MPC. In particular, we focus on the sparse identification of nonlinear dynamics (SINDy) algorithm and show how it may be used with MPC on an infectious disease control example. We compare the performance against MPC based on a linear dynamic mode decomposition (DMD) model. Code is provided to run the tutorial examples and may be modified to extend this data-driven control framework to arbitrary nonlinear systems.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/700552dde778bd1f6b76f7237e9b561597b859ba" target='_blank'>SINDy with Control: A Tutorial</a></td>
-          <td>
-            Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton
-          </td>
-          <td>2021-08-30</td>
-          <td>2021 60th IEEE Conference on Decision and Control (CDC)</td>
-          <td>51</td>
-          <td>66</td>
-        </tr>
-
         <tr id="In this work we analyze the effectiveness of the Sparse Identification of Nonlinear Dynamics (SINDy) technique on three benchmark datasets for nonlinear identification, to provide a better understanding of its suitability when tackling real dynamical systems. While SINDy can be an appealing strategy for pursuing physics-based learning, our analysis highlights difficulties in dealing with unobserved states and non-smooth dynamics. Due to the ubiquity of these features in real systems in general, and control applications in particular, we complement our analysis with hands-on approaches to tackle these issues in order to exploit SINDy also in these challenging contexts.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/99877062e70fae24c531d58aa880fa443ec10c9b" target='_blank'>SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study</a></td>
@@ -520,8 +508,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-04-05</td>
           <td>Proceedings. Mathematical, Physical, and Engineering Sciences</td>
-          <td>220</td>
-          <td>66</td>
+          <td>221</td>
+          <td>67</td>
         </tr>
 
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetries and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -556,8 +544,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
 
         <tr id="Automated data-driven modeling, the process of directly discovering the governing equations of a system from data, is increasingly being used across the scientific community. PySINDy is a Python package that provides tools for applying the sparse identification of nonlinear dynamics (SINDy) approach to data-driven model discovery. In this major update to PySINDy, we implement several advanced features that enable the discovery of more general differential equations from noisy and limited data. The library of candidate terms is extended for the identification of actuated systems, partial differential equations (PDEs), and implicit differential equations. Robust formulations, including the integral form of SINDy and ensembling techniques, are also implemented to improve performance for real-world data. Finally, we provide a range of new optimization algorithms, including several sparse regression techniques and algorithms to enforce and promote inequality constraints and stability. Together, these updates enable entirely new SINDy model discovery capabilities that have not been reported in the literature, such as constrained PDE identification and ensembling with different sparse regression optimizers.">
@@ -568,8 +556,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>116</td>
-          <td>66</td>
+          <td>119</td>
+          <td>67</td>
         </tr>
 
         <tr id="Identifying dynamical systems characterized by nonlinear parameters presents significant challenges in deriving mathematical models that enhance understanding of physics. Traditional methods, such as Sparse Identification of Nonlinear Dynamics (SINDy) and symbolic regression, can extract governing equations from observational data; however, they also come with distinct advantages and disadvantages. This paper introduces a novel method within the SINDy framework, termed ADAM-SINDy, which synthesizes the strengths of established approaches by employing the ADAM optimization algorithm. This facilitates the simultaneous optimization of nonlinear parameters and coefficients associated with nonlinear candidate functions, enabling precise parameter estimation without requiring prior knowledge of nonlinear characteristics such as trigonometric frequencies, exponential bandwidths, or polynomial exponents, thereby addressing a key limitation of SINDy. Through an integrated global optimization, ADAM-SINDy dynamically adjusts all unknown variables in response to data, resulting in an adaptive identification procedure that reduces the sensitivity to the library of candidate functions. The performance of the ADAM-SINDy methodology is demonstrated across a spectrum of dynamical systems, including benchmark coupled nonlinear ordinary differential equations such as oscillators, chaotic fluid flows, reaction kinetics, pharmacokinetics, as well as nonlinear partial differential equations (wildfire transport). The results demonstrate significant improvements in identifying parameterized dynamical systems and underscore the importance of concurrently optimizing all parameters, particularly those characterized by nonlinear parameters. These findings highlight the potential of ADAM-SINDy to extend the applicability of the SINDy framework in addressing more complex challenges in dynamical system identification.">
diff --git a/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html b/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html
index 0ee22917..45704074 100644
--- a/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html
+++ b/site/recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:23 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:54 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -485,7 +485,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2018-07-14</td>
           <td>ArXiv</td>
           <td>9</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
         <tr id="Sparse regression models are increasingly prevalent due to their ease of interpretability and superior out-of-sample performance. However, the exact model of sparse regression with an $\ell_0$ constraint restricting the support of the estimators is a challenging (\NP-hard) non-convex optimization problem. In this paper, we derive new strong convex relaxations for sparse regression. These relaxations are based on the ideal (convex-hull) formulations for rank-one quadratic terms with indicator variables. The new relaxations can be formulated as semidefinite optimization problems in an extended space and are stronger and more general than the state-of-the-art formulations, including the perspective reformulation and formulations with the reverse Huber penalty and the minimax concave penalty functions. Furthermore, the proposed rank-one strengthening can be interpreted as a \textit{non-separable, non-convex, unbiased} sparsity-inducing regularizer, which dynamically adjusts its penalty according to the shape of the error function without inducing bias for the sparse solutions. In our computational experiments with benchmark datasets, the proposed conic formulations are solved within seconds and result in near-optimal solutions (with 0.4\% optimality gap) for non-convex $\ell_0$-problems. Moreover, the resulting estimators also outperform alternative convex approaches from a statistical perspective, achieving high prediction accuracy and good interpretability.">
diff --git a/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html b/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html
index 38e77422..e9ea192e 100644
--- a/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html
+++ b/site/recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:44 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:25 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-04-23</td>
           <td>ArXiv</td>
-          <td>6</td>
+          <td>7</td>
           <td>50</td>
         </tr>
 
@@ -495,8 +495,8 @@ <h3 data-intro='Recommendations for the article'>
             Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan
           </td>
           <td>2023-07-07</td>
-          <td>IEEE transactions on pattern analysis and machine intelligence</td>
-          <td>80</td>
+          <td>IEEE Transactions on Pattern Analysis and Machine Intelligence</td>
+          <td>82</td>
           <td>50</td>
         </tr>
 
@@ -556,8 +556,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-02-17</td>
           <td>IEEE Transactions on Knowledge and Data Engineering</td>
-          <td>66</td>
-          <td>43</td>
+          <td>69</td>
+          <td>44</td>
         </tr>
 
         <tr id="Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets.">
diff --git a/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html b/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html
index b76ac9ab..3ceb2a75 100644
--- a/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html
+++ b/site/recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:36 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:11 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -532,8 +532,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-24</td>
           <td>Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining</td>
-          <td>1075</td>
-          <td>56</td>
+          <td>1082</td>
+          <td>57</td>
         </tr>
 
         <tr id="Graph Neural Networks (GNN) have gained significant traction in the forecasting domain, especially for their capacity to simultaneously account for intra-series temporal correlations and inter-series relationships. This paper introduces a novel Hierarchical GNN (DeepHGNN) framework, explicitly designed for forecasting in complex hierarchical structures. The uniqueness of DeepHGNN lies in its innovative graph-based hierarchical interpolation and an end-to-end reconciliation mechanism. This approach ensures forecast accuracy and coherence across various hierarchical levels while sharing signals across them, addressing a key challenge in hierarchical forecasting. A critical insight in hierarchical time series is the variance in forecastability across levels, with upper levels typically presenting more predictable components. DeepHGNN capitalizes on this insight by pooling and leveraging knowledge from all hierarchy levels, thereby enhancing the overall forecast accuracy. Our comprehensive evaluation set against several state-of-the-art models confirm the superior performance of DeepHGNN. This research not only demonstrates DeepHGNN's effectiveness in achieving significantly improved forecast accuracy but also contributes to the understanding of graph-based methods in hierarchical time series forecasting.">
@@ -556,7 +556,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>46</td>
+          <td>49</td>
           <td>6</td>
         </tr>
 
diff --git a/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html b/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html
index cd24c581..9efdc9fb 100644
--- a/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html
+++ b/site/recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:33 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:07 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -488,6 +488,18 @@ <h3 data-intro='Recommendations for the article'>
           <td>15</td>
         </tr>
 
+        <tr id="In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture dependencies among variables and across time points. The objective of the presented systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and over 150 journal papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive collection of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in future studies. To the best of our knowledge, this is the first systematic literature review presenting a detailed comparison of the results of current spatio-temporal GNN models in different domains. In addition, in its final part this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/e6544d542d13247f620834689ca2378cb7fb04f8" target='_blank'>A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification</a></td>
+          <td>
+            Flavio Corradini, Marco Gori, Carlo Lucheroni, Marco Piangerelli, Martina Zannotti
+          </td>
+          <td>2024-10-29</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>12</td>
+        </tr>
+
         <tr id="Time series forecasting lies at the core of important real-world applications in many fields of science and engineering. The abundance of large time series datasets that consist of complex patterns and long-term dependencies has led to the development of various neural network architectures. Graph neural network approaches, which jointly learn a graph structure based on the correlation of raw values of multivariate time series while forecasting, have recently seen great success. However, such solutions are often costly to train and difficult to scale. In this paper, we propose TimeGNN, a method that learns dynamic temporal graph representations that can capture the evolution of inter-series patterns along with the correlations of multiple series. TimeGNN achieves inference times 4 to 80 times faster than other state-of-the-art graph-based methods while achieving comparable forecasting performance">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/d9916caa8da69496ecc8e0b1c229c73a60d6f861" target='_blank'>TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting</a></td>
@@ -508,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-24</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>9</td>
           <td>50</td>
         </tr>
 
@@ -544,7 +556,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>46</td>
+          <td>49</td>
           <td>6</td>
         </tr>
 
@@ -584,18 +596,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>1</td>
         </tr>
 
-        <tr id="This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification, based on visibility graph representations. Traditional time series classification methods often struggle with high computational complexity and inadequate capture of spatio-temporal dynamics. By representing time series as visibility graphs, it is possible to encode both spatial and temporal dependencies inherent to time series data, while being computationally efficient. Our architecture is fully modular, enabling flexible experimentation with different models and representations. We employ directed visibility graphs encoded with in-degree and PageRank features to improve the representation of time series, ensuring efficient computation while enhancing the model's ability to capture long-range dependencies in the data. We show the robustness and generalization capability of the proposed architecture across a diverse set of classification tasks and against a traditional model. Our work represents a significant advancement in the application of GNNs for time series analysis, offering a powerful and flexible framework for future research and practical implementations.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/1be874b113152ade7584757402c8129e5e036526" target='_blank'>Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification</a></td>
-          <td>
-            Paulo Coelho, Raul Araju, Lu'is Ramos, Samir Saliba, Renato Vimieiro
-          </td>
-          <td>2024-10-12</td>
-          <td>ArXiv</td>
-          <td>0</td>
-          <td>0</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html b/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html
index a1c05eb5..4747e1ac 100644
--- a/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html
+++ b/site/recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:35 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:10 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,7 +484,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-24</td>
           <td>ArXiv</td>
-          <td>8</td>
+          <td>9</td>
           <td>50</td>
         </tr>
 
@@ -496,7 +496,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2022-09-14</td>
           <td>ArXiv</td>
-          <td>33</td>
+          <td>34</td>
           <td>50</td>
         </tr>
 
@@ -544,7 +544,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-11-10</td>
           <td>ArXiv</td>
-          <td>46</td>
+          <td>49</td>
           <td>6</td>
         </tr>
 
diff --git a/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html b/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html
index 36ed7a1d..87ffd3db 100644
--- a/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html
+++ b/site/recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:42 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:19 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -488,18 +488,6 @@ <h3 data-intro='Recommendations for the article'>
           <td>3</td>
         </tr>
 
-        <tr id="We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning diverse domains. In our proposed framework, the task of multivariate time series forecasting is formulated as a channel-wise next curve shape prediction problem, where each time series sample is represented as a sequence of non-overlapping curve shapes with a unified numerical magnitude. GTT is trained to predict the next curve shape based on a window of past curve shapes in a channel-wise manner. Experimental results demonstrate that GTT exhibits superior zero-shot multivariate forecasting capabilities on unseen time series datasets, even surpassing state-of-the-art supervised baselines. Additionally, we investigate the impact of varying GTT model parameters and training dataset scales, observing that the scaling law also holds in the context of zero-shot multivariate time series forecasting.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/15ea0190593662402d0493958a53d5ca3b8e8406" target='_blank'>Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction</a></td>
-          <td>
-            Cheng Feng, Long Huang, Denis Krompass
-          </td>
-          <td>2024-02-12</td>
-          <td>ArXiv</td>
-          <td>4</td>
-          <td>15</td>
-        </tr>
-
         <tr id="In recent years, generative pre-trained paradigms such as Large Language Models (LLMs) and Large Vision Models (LVMs) have achieved revolutionary advancements and widespread real-world applications. Particularly, the emergence of pre-trained LLMs-based temporal works, compared to previous deep model approaches, has demonstrated superior generalization and robustness, showcasing the potential of generative pre-trained paradigms as foundation models for time series. However, those LLMs-based works mainly focus on cross-modal research, i.e., leveraging the language capabilities of LLMs in time series contexts. Although they have achieved impressive performance, there still exist the issues of concept drift caused by differences in data distribution and inflexibility caused by misalignment of dimensions. To this end, inspired by recent work on LVMs, we reconsider the paradigm of time series modeling. In this paper, we comprehensively explore, for the first time, the effectiveness and superiority of the Generative Pre-trained Diffusion (GPD) paradigm in real-world multivariate time series forecasting (TSF). Specifically, to mitigate performance bias introduced by sophisticated networks, we propose a straightforward MLP diffusion network for unconditional modeling of time series. Then we employ a zero-shot and tuning-free method to predict (generate) future data using historical data as prompts. The GPD paradigm is established on the time series modality, effectively preventing the phenomenon of concept drift, and enabling flexible forecasting of arbitrary lengths. We demonstrate that the GPD paradigm achieves comprehensive performance and generalization comparable to current SOTA LLM-based and deep model paradigms on mainstream benchmarks and various TSF tasks. Extensive experiments validate the potential of the GPD paradigm and its assistance in future related research.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/818793961396bbe4c744932676f0ff9cc8385bed" target='_blank'>Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting</a></td>
@@ -512,16 +500,16 @@ <h3 data-intro='Recommendations for the article'>
           <td>5</td>
         </tr>
 
-        <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
+        <tr id="We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M high-quality time series samples spanning diverse domains. In our proposed framework, the task of multivariate time series forecasting is formulated as a channel-wise next curve shape prediction problem, where each time series sample is represented as a sequence of non-overlapping curve shapes with a unified numerical magnitude. GTT is trained to predict the next curve shape based on a window of past curve shapes in a channel-wise manner. Experimental results demonstrate that GTT exhibits superior zero-shot multivariate forecasting capabilities on unseen time series datasets, even surpassing state-of-the-art supervised baselines. Additionally, we investigate the impact of varying GTT model parameters and training dataset scales, observing that the scaling law also holds in the context of zero-shot multivariate time series forecasting.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/15ea0190593662402d0493958a53d5ca3b8e8406" target='_blank'>Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction</a></td>
           <td>
-            Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
+            Cheng Feng, Long Huang, Denis Krompass
           </td>
-          <td>2024-03-12</td>
+          <td>2024-02-12</td>
           <td>ArXiv</td>
-          <td>63</td>
-          <td>18</td>
+          <td>4</td>
+          <td>15</td>
         </tr>
 
         <tr id="Over the past years, foundation models have caused a paradigm shift in machine learning due to their unprecedented capabilities for zero-shot and few-shot generalization. However, despite the success of foundation models in modalities such as natural language processing and computer vision, the development of foundation models for time series forecasting has lagged behind. We present Lag-Llama, a general-purpose foundation model for univariate probabilistic time series forecasting based on a decoder-only transformer architecture that uses lags as covariates. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities compared to a wide range of forecasting models on downstream datasets across domains. Moreover, when fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance, outperforming prior deep learning approaches, emerging as the best general-purpose model on average. Lag-Llama serves as a strong contender to the current state-of-art in time series forecasting and paves the way for future advancements in foundation models tailored to time series data.">
@@ -532,10 +520,22 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>23</td>
           <td>40</td>
         </tr>
 
+        <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td>
+            Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
+          </td>
+          <td>2024-03-12</td>
+          <td>ArXiv</td>
+          <td>69</td>
+          <td>18</td>
+        </tr>
+
         <tr id="Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training and struggle with poor generalization performance on new domains. Foundation models aim to overcome this limitation. Pre-trained on large-scale language or time series data, they exhibit promising inferencing capabilities in new or unseen data. This has spurred a surge in new TSF foundation models. We propose a new benchmark, FoundTS, to enable thorough and fair evaluation and comparison of such models. FoundTS covers a variety of TSF foundation models, including those based on large language models and those pretrained on time series. Next, FoundTS supports different forecasting strategies, including zero-shot, few-shot, and full-shot, thereby facilitating more thorough evaluations. Finally, FoundTS offers a pipeline that standardizes evaluation processes such as dataset splitting, loading, normalization, and few-shot sampling, thereby facilitating fair evaluations. Building on this, we report on an extensive evaluation of TSF foundation models on a broad range of datasets from diverse domains and with different statistical characteristics. Specifically, we identify pros and cons and inherent limitations of existing foundation models, and we identify directions for future model design. We make our code and datasets available at https://anonymous.4open.science/r/FoundTS-C2B0.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/c245a7d561855d7bef6c758e45e772129b52dacb" target='_blank'>FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting</a></td>
@@ -569,7 +569,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2024-02-07</td>
           <td>ArXiv</td>
           <td>8</td>
-          <td>7</td>
+          <td>8</td>
         </tr>
 
   </tbody>
diff --git a/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html b/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html
index 8fb7d35e..4af81ec8 100644
--- a/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html
+++ b/site/recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:06:20 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:53 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -496,8 +496,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2015-09-11</td>
           <td>Proceedings of the National Academy of Sciences</td>
-          <td>3383</td>
-          <td>66</td>
+          <td>3408</td>
+          <td>67</td>
         </tr>
 
         <tr id="Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetries and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.">
@@ -520,8 +520,8 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2021-11-12</td>
           <td>J. Open Source Softw.</td>
-          <td>116</td>
-          <td>66</td>
+          <td>119</td>
+          <td>67</td>
         </tr>
 
         <tr id="Sparse regression has emerged as a popular technique for learning dynamical systems from temporal data, beginning with the SINDy (Sparse Identification of Nonlinear Dynamics) framework proposed by arXiv:1509.03580. Quantifying the uncertainty inherent in differential equations learned from data remains an open problem, thus we propose leveraging recent advances in statistical inference for sparse regression to address this issue. Focusing on systems of ordinary differential equations (ODEs), SINDy assumes that each equation is a parsimonious linear combination of a few candidate functions, such as polynomials, and uses methods such as sequentially-thresholded least squares or the Lasso to identify a small subset of these functions that govern the system's dynamics. We instead employ bias-corrected versions of the Lasso and ridge regression estimators, as well as an empirical Bayes variable selection technique known as SEMMS, to estimate each ODE as a linear combination of terms that are statistically significant. We demonstrate through simulations that this approach allows us to recover the functional terms that correctly describe the dynamics more often than existing methods that do not account for uncertainty.">
@@ -544,7 +544,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2020-05-05</td>
           <td>Nature Communications</td>
-          <td>282</td>
+          <td>289</td>
           <td>13</td>
         </tr>
 
@@ -557,7 +557,7 @@ <h3 data-intro='Recommendations for the article'>
           <td>2020-04-17</td>
           <td>arXiv: Dynamical Systems</td>
           <td>51</td>
-          <td>66</td>
+          <td>67</td>
         </tr>
 
         <tr id="Data-driven modeling frameworks that adopt sparse regression techniques, such as sparse identification of nonlinear dynamics (SINDy) and its modifications, are developed to resolve difficulties in extracting underlying dynamics from experimental data. In contrast to neural-network-based methods, these methods are designed to obtain white-box analytical models. In this work, we incorporate the concept of SINDy and knowledge in the field of classical mechanics to identify interpretable and sparse expressions of total energy and the Lagrangian that shelters the hidden dynamics. Moreover, our method (hereafter referred as Lagrangian-SINDy) is developed to use knowledge of simple systems that form the system being analyzed to ensure the likelihood of correct results and to improve the learning pace. Lagrangian-SINDy is highly accurate in discovering interpretable dynamics via energy-related physical quantities. Its performance is validated with three popular multi-DOF nonlinear dynamical systems, namely the spherical pendulum, double pendulum and cart-pendulum system. Comparisons with other SINDy-based methods are made and Lagrangian-SINDy is found to provide the most compact analytical models.">
@@ -580,22 +580,10 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2017-12-06</td>
           <td>The Journal of chemical physics</td>
-          <td>199</td>
+          <td>200</td>
           <td>46</td>
         </tr>
 
-        <tr id="Discovering the partial differential equations underlying spatio-temporal datasets from very limited and highly noisy observations is of paramount interest in many scientific fields. However, it remains an open question to know when model discovery algorithms based on sparse regression can actually recover the underlying physical processes. In this work, we show the design matrices used to infer the equations by sparse regression can violate the irrepresentability condition (IRC) of the Lasso, even when derived from analytical PDE solutions (i.e. without additional noise). Sparse regression techniques which can recover the true underlying model under violated IRC conditions are therefore required, leading to the introduction of the randomised adaptive Lasso. We show once the latter is integrated within the deep learning model discovery framework DeepMod, a wide variety of nonlinear and chaotic canonical PDEs can be recovered: (1) up to $\mathcal{O}(2)$ higher noise-to-sample ratios than state-of-the-art algorithms, (2) with a single set of hyperparameters, which paves the road towards truly automated model discovery.">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/45633bde87d3fecdb0ef577036f2465e70c51f24" target='_blank'>Sparsistent Model Discovery</a></td>
-          <td>
-            Georges Tod, G. Both, R. Kusters
-          </td>
-          <td>2021-06-22</td>
-          <td>ArXiv</td>
-          <td>1</td>
-          <td>13</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html b/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html
index 86dea626..a18e2cf9 100644
--- a/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html
+++ b/site/recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:39 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:15 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -484,10 +484,22 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-12</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>23</td>
           <td>40</td>
         </tr>
 
+        <tr id="Motivated by the recent success of time-series foundation models for zero-shot forecasting, we present a methodology for $\textit{in-context fine-tuning}$ of a time-series foundation model. In particular, we design a pretrained foundation model that can be prompted (at inference time) with multiple time-series examples, in order to forecast a target time-series into the future. Our foundation model is specifically trained to utilize examples from multiple related time-series in its context window (in addition to the history of the target time-series) to help it adapt to the specific distribution of the target domain at inference time. We show that such a foundation model that uses in-context examples at inference time can obtain much better performance on popular forecasting benchmarks compared to supervised deep learning methods, statistical models, as well as other time-series foundation models. Interestingly, our in-context fine-tuning approach even rivals the performance of a foundation model that is explicitly fine-tuned on the target domain.">
+          <td id="tag"><i class="material-icons">visibility_off</i></td>
+          <td><a href="https://www.semanticscholar.org/paper/1216a29c0beb36a2b621b59d36877edf05afcf8c" target='_blank'>In-Context Fine-Tuning for Time-Series Foundation Models</a></td>
+          <td>
+            Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou
+          </td>
+          <td>2024-10-31</td>
+          <td>ArXiv</td>
+          <td>0</td>
+          <td>14</td>
+        </tr>
+
         <tr id="Large language models (LLMs) are being applied to time series forecasting. But are language models actually useful for time series? In a series of ablation studies on three recent and popular LLM-based time series forecasting methods, we find that removing the LLM component or replacing it with a basic attention layer does not degrade forecasting performance -- in most cases, the results even improve! We also find that despite their significant computational cost, pretrained LLMs do no better than models trained from scratch, do not represent the sequential dependencies in time series, and do not assist in few-shot settings. Additionally, we explore time series encoders and find that patching and attention structures perform similarly to LLM-based forecasters.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
           <td><a href="https://www.semanticscholar.org/paper/df0d604b8e8e3b2947d9865d735f204c08635012" target='_blank'>Are Language Models Actually Useful for Time Series Forecasting?</a></td>
@@ -496,7 +508,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-06-22</td>
           <td>ArXiv</td>
-          <td>11</td>
+          <td>13</td>
           <td>3</td>
         </tr>
 
@@ -520,7 +532,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-10-03</td>
           <td>ArXiv</td>
-          <td>184</td>
+          <td>195</td>
           <td>9</td>
         </tr>
 
@@ -556,7 +568,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2023-08-16</td>
           <td>ArXiv</td>
-          <td>21</td>
+          <td>22</td>
           <td>3</td>
         </tr>
 
@@ -568,22 +580,10 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-09-24</td>
           <td>ArXiv</td>
-          <td>1</td>
+          <td>2</td>
           <td>9</td>
         </tr>
 
-        <tr id="Time series data are ubiquitous across various domains, making time series analysis critically important. Traditional time series models are task-specific, featuring singular functionality and limited generalization capacity. Recently, large language foundation models have unveiled their remarkable capabilities for cross-task transferability, zero-shot/few-shot learning, and decision-making explainability. This success has sparked interest in the exploration of foundation models to solve multiple time series challenges simultaneously. There are two main research lines, namely pre-training foundation models from scratch for time series and adapting large language foundation models for time series. They both contribute to the development of a unified model that is highly generalizable, versatile, and comprehensible for time series analysis. This survey offers a 3E analytical framework for comprehensive examination of related research. Specifically, we examine existing works from three dimensions, namely Effectiveness, Efficiency and Explainability. In each dimension, we focus on discussing how related works devise tailored solution by considering unique challenges in the realm of time series. Furthermore, we provide a domain taxonomy to help followers keep up with the domain-specific advancements. In addition, we introduce extensive resources to facilitate the field's development, including datasets, open-source, time series libraries. A GitHub repository is also maintained for resource updates (https://github.com/start2020/Awesome-TimeSeries-LLM-FM).">
-          <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/522828823ff726598675971036fff01b57320578" target='_blank'>A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model</a></td>
-          <td>
-            Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, F. Tsung
-          </td>
-          <td>2024-05-03</td>
-          <td>ArXiv</td>
-          <td>7</td>
-          <td>47</td>
-        </tr>
-
   </tbody>
   <tfoot>
     <tr>
diff --git a/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html b/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html
index 41fe4304..c5ccad58 100644
--- a/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html
+++ b/site/recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/index.html
@@ -445,7 +445,7 @@ <h1 id="_1"></h1>
 
 <body>
   <p>
-  <i class="footer">This page was last updated on 2024-11-04 06:05:48 UTC</i>
+  <i class="footer">This page was last updated on 2024-11-11 06:05:30 UTC</i>
   </p>
 
   <div class="note info" onclick="startIntro()">
@@ -483,8 +483,8 @@ <h3 data-intro='Recommendations for the article'>
             Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long
           </td>
           <td>2024-02-04</td>
-          <td>DBLP, ArXiv</td>
-          <td>18</td>
+          <td>ArXiv, DBLP</td>
+          <td>21</td>
           <td>67</td>
         </tr>
 
@@ -520,7 +520,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-26</td>
           <td>ArXiv</td>
-          <td>6</td>
+          <td>7</td>
           <td>4</td>
         </tr>
 
@@ -550,13 +550,13 @@ <h3 data-intro='Recommendations for the article'>
 
         <tr id="We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters) on a large collection of publicly available datasets, complemented by a synthetic dataset that we generated via Gaussian processes to improve generalization. In a comprehensive benchmark consisting of 42 datasets, and comprising both classical local models and deep learning methods, we show that Chronos models: (a) significantly outperform other methods on datasets that were part of the training corpus; and (b) have comparable and occasionally superior zero-shot performance on new datasets, relative to methods that were trained specifically on them. Our results demonstrate that Chronos models can leverage time series data from diverse domains to improve zero-shot accuracy on unseen forecasting tasks, positioning pretrained models as a viable tool to greatly simplify forecasting pipelines.">
           <td id="tag"><i class="material-icons">visibility_off</i></td>
-          <td><a href="https://www.semanticscholar.org/paper/4fb78450650894091b0a55b5504a8b0a6f3dec37" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
+          <td><a href="https://www.semanticscholar.org/paper/02fa77e4f355198cb4270f6d4a07517bf09c46dd" target='_blank'>Chronos: Learning the Language of Time Series</a></td>
           <td>
             Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang
           </td>
           <td>2024-03-12</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>69</td>
           <td>18</td>
         </tr>
 
@@ -568,7 +568,7 @@ <h3 data-intro='Recommendations for the article'>
           </td>
           <td>2024-02-04</td>
           <td>ArXiv</td>
-          <td>63</td>
+          <td>71</td>
           <td>25</td>
         </tr>
 
diff --git a/site/search/search_index.json b/site/search/search_index.json
index 42c292f6..093b2abb 100644
--- a/site/search/search_index.json
+++ b/site/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":"Introduction <p>Welcome to Team VPE's Literature Survey System! This project leverages the powerful Semantic Scholar's Recommendation API to provide you with highly relevant research article recommendations based on your curated lists of articles.</p> <p>Moreover, this website contains curated collection of references on differentiable and learned simulator algorithms for developing digital twins with applications in drug discovery and development.</p> Features <ul> <li>List-based recommendations: For each topic, we use a manually curated list of positive articles. We then select an equal number of negative articles from other topics. Semantics Scholar's recommendation system uses the set of positive and negative to provide up to 300 recommendations per topic.</li> <li>Single-paper recommendations: For each article in our curated lists, Semantics Scholar's recommendation system returns up to 10 related articles.</li> </ul> How it works? <ul> <li>Scroll to the top of this page and click one of the tabs to open the relevant section.</li> <li>Each topic contains two tables. The first table lists the manually curated articles, and the second table lists the articles recommended for that topic by the recommendation system. The last column of the first table contains a hyperlink that opens a new tab with articles recommended for that specific article.</li> <li>Click on the help icon at the top of the page to learn more about that section.</li> </ul>         Check out our GitHub open_in_new repository to learn how to build one for yourself."},{"location":"Symbolic%20regression/","title":"Symbolic regression","text":"<p> This page was last updated on 2024-11-04 06:06:38 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p> </p> <p> </p>"},{"location":"Symbolic%20regression/#manually_curated_articles","title":"Manually curated articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index                View recommendations                visibility_off                  Discovering governing equations from data by sparse identification of nonlinear dynamical systems                               S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences of the United States of America 3383 66 open_in_new visibility_off                  Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression                               Patrick A. K. Reinbold, Logan Kageorge, M. Schatz, R. Grigoriev            2021-02-24 Nature Communications 91 24 open_in_new visibility_off                  Data-driven discovery of coordinates and governing equations                               Kathleen P. Champion, Bethany Lusch, J. Kutz, S. Brunton            2019-03-29 Proceedings of the National Academy of Sciences of the United States of America 645 66 open_in_new visibility_off                  Chaos as an intermittently forced linear system                               S. Brunton, Bingni W. Brunton, J. Proctor, E. Kaiser, J. Kutz            2016-08-18 Nature Communications 462 66 open_in_new visibility_off                  Sparse identification of nonlinear dynamics for model predictive control in the low-data limit                               E. Kaiser, J. Kutz, S. Brunton            2017-11-15 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 455 66 open_in_new visibility_off                  Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics                               N. Mangan, S. Brunton, J. Proctor, J. Kutz            2016-05-26 IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE Transactions on Molecular Biological and Multi-Scale Communications 333 66 open_in_new visibility_off                  SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics                               Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 220 66 open_in_new visibility_off                  Multidimensional Approximation of Nonlinear Dynamical Systems                               Patrick Gel\u00df, Stefan Klus, J. Eisert, Christof Schutte            2018-09-07 Journal of Computational and Nonlinear Dynamics 64 79 open_in_new visibility_off                  Learning Discrepancy Models From Experimental Data                               Kadierdan Kaheman, E. Kaiser, B. Strom, J. Kutz, S. Brunton            2019-09-18 ArXiv, arXiv.org 35 66 open_in_new visibility_off                  Discovery of Physics From Data: Universal Laws and Discrepancies                               Brian M. de Silva, D. Higdon, S. Brunton, J. Kutz            2019-06-19 Frontiers in Artificial Intelligence 75 66 open_in_new visibility_off                  Data-driven discovery of partial differential equations                               S. Rudy, S. Brunton, J. Proctor, J. Kutz            2016-09-21 Science Advances 1229 66 open_in_new visibility_off                  Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control                               Urban Fasel, J. Kutz, Bingni W. Brunton, S. Brunton            2021-11-22 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 178 66 open_in_new visibility_off                  Learning sparse nonlinear dynamics via mixed-integer optimization                               D. Bertsimas, Wes Gurnee            2022-06-01 Nonlinear Dynamics 30 92 open_in_new visibility_off                  A Unified Framework for Sparse Relaxed Regularized Regression: SR3                               P. Zheng, T. Askham, S. Brunton, J. Kutz, A. Aravkin            2018-07-14 IEEE Access 123 66 open_in_new Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index View recommendations"},{"location":"Symbolic%20regression/#recommended_articles","title":"Recommended articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index visibility_off                ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification                             Siva Viknesh, Younes Tatari, Amirhossein Arzani            2024-10-21 ArXiv 0 24 visibility_off                Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems                             Doris Voina, S. Brunton, J. Kutz            2024-10-02 ArXiv 0 66 visibility_off                Weak-form modified sparse identification of nonlinear dynamics                             Cristian L'opez, 'Angel Naranjo, Diego Salazar, Keegan J. Moore            2024-10-23 ArXiv 0 1 visibility_off                Data-driven model discovery and model selection for noisy biological systems                             Xiaojun Wu, MeiLu McDermott, Adam L. Maclean            2024-10-04 bioRxiv 0 19 visibility_off                Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems                             A. Pal, Sutanu Bhowmick, Satish Nagarajaiah            2024-10-13 ArXiv 0 9 visibility_off                Parsimonious Dynamic Mode Decomposition: A Robust and Automated Approach for Optimally Sparse Mode Selection in Complex Systems                             Arpan Das, Pier Marzocca, O. 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Zitnik            2024-02-29 ArXiv 4 47 open_in_new Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index View recommendations"},{"location":"Time-series%20forecasting/#recommended_articles","title":"Recommended articles on Time-series forecasting","text":"Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index visibility_off                Metadata Matters for Time Series: Informative Forecasting with Transformers                             Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Li Zhang, Jianmin Wang, Mingsheng Long            2024-10-04 ArXiv 0 67 visibility_off                FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities                             Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl, Simon Gottschalk            2024-10-30 ArXiv 0 6 visibility_off                Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts                             X. 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Sundaram            2023-02-08 ArXiv 7 36 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/883547fdbd88552328a6615ec620f96e39c57018/","title":"883547fdbd88552328a6615ec620f96e39c57018","text":""},{"location":"recommendations/883547fdbd88552328a6615ec620f96e39c57018/#_1","title":"883547fdbd88552328a6615ec620f96e39c57018","text":"<p> This page was last updated on 2024-11-04 06:06:19 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Sparsifying priors for Bayesian uncertainty quantification in model discovery              Seth M. Hirsh, D. Barajas-Solano, J. Kutz            2021-07-05 Royal Society Open Science 59 32 visibility_off Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery              Liyao (Mars) Gao, Urban Fasel, S. Brunton, J. Kutz            2023-01-30 ArXiv 11 66 visibility_off Automatically discovering ordinary differential equations from data with sparse regression              Kevin Egan, Weizhen Li, Rui Carvalho            2024-01-09 Communications Physics 11 2 visibility_off SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics              Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings. Mathematical, Physical, and Engineering Sciences 220 66 visibility_off Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data              Lloyd Fung, Urban Fasel, M. Juniper            2024-02-23 ArXiv 1 38 visibility_off Discovering governing equations from data by sparse identification of nonlinear dynamical systems              S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences 3383 66 visibility_off Sparse identification of nonlinear dynamics in the presence of library and system uncertainty              Andrew O'Brien            2024-01-23 ArXiv 0 0 visibility_off Comparative Analysis of Uncertainty Quantification Models in Active Learning for Efficient System Identification of Dynamical Systems              Hans Mertens, Frances Zhu            2024-08-28 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) 0 0 $\\dot { \\boldsymbol x} = { \\boldsymbol f} ({ \\boldsymbol x})$ . First, we propose, for use in high-noise settings, an extensive toolkit of critically enabling extensions for the SINDy regression method, to progressively cull functionals from an over-complete library and yield a set of sparse equations that regress to the derivate  $\\dot { \\boldsymbol {x}}$ . This toolkit includes: (regression step) weight timepoints based on estimated noise, use ensembles to estimate coefficients, and regress using FFTs; (culling step) leverage linear dependence of functionals, and restore and protect culled functionals based on Figures of Merit (FoMs). In a novel Assessment step, we define FoMs that compare model predictions to the original time-series (i.e.,  ${ \\boldsymbol x}(t)$  rather than  $\\dot { \\boldsymbol {x}}(t)$ ). These innovations can extract sparse governing equations and coefficients from high-noise time-series data (e.g., 300% added noise). For example, it discovers the correct sparse libraries in the Lorenz system, with median coefficient estimate errors equal to 1%\u22123% (for 50% noise), 6%\u22128% (for 100% noise), and 23%\u221225% (for 300% noise). The enabling modules in the toolkit are combined into a single method, but the individual modules can be tactically applied in other equation discovery methods (SINDy or not) to improve results on high-noise data. Second, we propose a technique, applicable to any model discovery method based on  $\\dot { \\boldsymbol x} = { \\boldsymbol f} ({ \\boldsymbol x})$ , to assess the accuracy of a discovered model in the context of non-unique solutions due to noisy data. Currently, this non-uniqueness can obscure a discovered model\u2019s accuracy and thus a discovery method\u2019s effectiveness. We describe a technique that uses linear dependencies among functionals to transform a discovered model into an equivalent form that is closest to the true model, enabling more accurate assessment of a discovered model\u2019s correctness.\"&gt;           visibility_off A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes              Charles B. Delahunt, J. Kutz            2021-11-08 IEEE Access 16 32 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/","title":"B2eb064f432557c59ce99834d7dc7817e4687271","text":""},{"location":"recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/#_1","title":"B2eb064f432557c59ce99834d7dc7817e4687271","text":"<p> This page was last updated on 2024-11-04 06:06:04 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Sparse identification of nonlinear dynamics for model predictive control in the low-data limit Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off SINDy with Control: A Tutorial              Urban Fasel, E. Kaiser, J. Kutz, Bingni W. Brunton, S. Brunton            2021-08-30 2021 60th IEEE Conference on Decision and Control (CDC) 51 66 visibility_off SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study              Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni, M. Tanelli            2024-03-01 ArXiv 1 2 visibility_off Discovering Interpretable Dynamics by Sparsity Promotion on Energy and the Lagrangian              H. Chu, M. Hayashibe            2020-01-31 IEEE Robotics and Automation Letters 24 24 visibility_off SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics              Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings. Mathematical, Physical, and Engineering Sciences 220 66 visibility_off Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI)              Gabriel F. Machado, Morgan Jones            2023-10-06 2024 American Control Conference (ACC) 1 1 visibility_off Multi-objective SINDy for parameterized model discovery from single transient trajectory data              Javier A. Lemus, Benjamin Herrmann            2024-05-14 ArXiv 0 0 visibility_off Discovering governing equations from data by sparse identification of nonlinear dynamical systems              S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences 3383 66 visibility_off PySINDy: A comprehensive Python package for robust sparse system identification              A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Kathleen P. Champion, Jean-Christophe Loiseau, J. Kutz, S. Brunton            2021-11-12 J. Open Source Softw. 116 66 visibility_off ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification              Siva Viknesh, Younes Tatari, Amirhossein Arzani            2024-10-21 ArXiv 0 24 visibility_off Nonlinear Control of Networked Dynamical Systems              Megan Morrison, Nathan Kutz            2020-06-09 IEEE Transactions on Network Science and Engineering 10 5 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/","title":"C0fc3882a9976f6a9cdc3a724bce184b786503da","text":""},{"location":"recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/#_1","title":"C0fc3882a9976f6a9cdc3a724bce184b786503da","text":"<p> This page was last updated on 2024-11-04 06:06:23 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article A Unified Framework for Sparse Relaxed Regularized Regression: SR3 Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Sparse Relaxed Regularized Regression: SR3              P. Zheng, T. Askham, S. Brunton, J. Kutz, A. Aravkin            2018-07-14 ArXiv 9 66 visibility_off Rank-one Convexification for Sparse Regression              Alper Atamt\u00fcrk, A. G\u00f3mez            2019-01-29 ArXiv 48 36 visibility_off Sparse Recovery via Partial Regularization: Models, Theory and Algorithms              Zhaosong Lu, Xiaorui Li            2015-11-23 ArXiv 37 32 visibility_off Structured Regularizers for High-Dimensional Problems: Statistical and Computational Issues              M. Wainwright            2014-01-03 59 95 visibility_off Compressed Sparse Linear Regression              S. Kasiviswanathan, M. Rudelson            2017-07-25 ArXiv 1 30 visibility_off WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditions              Matthew J. Colbrook            2021-10-24 ArXiv 2 17 $\\ell _1$ -norm as the loss function for the residual error and utilizes a generalized nonconvex penalty for sparsity inducing. The $\\ell _1$ -loss is less sensitive to outliers in the measurements than the popular  $\\ell _2$-loss, while the nonconvex penalty has the capability of ameliorating the bias problem of the popular convex LASSO penalty and thus can yield more accurate recovery. To solve this nonconvex and nonsmooth minimization formulation efficiently, we propose a first-order algorithm based on alternating direction method of multipliers. A smoothing strategy on the $\\ell _1$ -loss function has been used in deriving the new algorithm to make it convergent. Further, a sufficient condition for the convergence of the new algorithm has been provided for generalized nonconvex regularization. In comparison with several state-of-the-art algorithms, the new algorithm showed better performance in numerical experiments in recovering sparse signals and compressible images. The new algorithm scales well for large-scale problems, as often encountered in image processing.\"&gt;           visibility_off Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization              Fei Wen, L. Pei, Yuan Yang, Wenxian Yu, Peilin Liu            2017-03-23 IEEE Transactions on Computational Imaging 87 31 visibility_off Regularizers for structured sparsity              C. Micchelli, Jean Morales, M. Pontil            2010-10-04 Advances in Computational Mathematics 80 70 visibility_off Regularizers for structured sparsity              C. Micchelli, Jean Morales, M. Pontil            2010-10-04 Advances in Computational Mathematics 80 70 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/","title":"C3c94ccc094dcf546e8e31c9a42506302e837524","text":""},{"location":"recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/#_1","title":"C3c94ccc094dcf546e8e31c9a42506302e837524","text":"<p> This page was last updated on 2024-11-04 06:05:44 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs              Daniele Zambon, C. Alippi            2022-04-23 ArXiv 6 50 visibility_off A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection              Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan            2023-07-07 IEEE transactions on pattern analysis and machine intelligence 80 50 visibility_off Learning Time-Aware Graph Structures for Spatially Correlated Time Series Forecasting              Minbo Ma, Jilin Hu, Christian S. Jensen, Fei Teng, Peng Han, Zhiqiang Xu, Tian-Jie Li            2023-12-27 2024 IEEE 40th International Conference on Data Engineering (ICDE) 0 13 visibility_off Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches              Stefan Bloemheuvel, J. 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Yang, Shirui Pan            2022-02-17 IEEE Transactions on Knowledge and Data Engineering 66 43 visibility_off Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection              Yuyin Tian, Rong Gao, Lingyu Yan, Donghua Liu, Zhiwei Ye            2023-09-07 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 0 4 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/","title":"Ccea298edb788edf821aef58f0952c3e8debc25a","text":""},{"location":"recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/#_1","title":"Ccea298edb788edf821aef58f0952c3e8debc25a","text":"<p> This page was last updated on 2024-11-04 06:05:36 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Graph Deep Learning for Time Series Forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting              Nancy R. Xu, Chrysoula Kosma, M. Vazirgiannis            2023-07-27 ArXiv 1 55 visibility_off Balanced Graph Structure Learning for Multivariate Time Series Forecasting              Weijun Chen, Yanze Wang, Chengshuo Du, Zhenglong Jia, Feng Liu, Ran Chen            2022-01-24 ArXiv 0 11 visibility_off Sparse Graph Learning from Spatiotemporal Time Series              Andrea Cini, Daniele Zambon, C. Alippi            2022-05-26 J. Mach. Learn. Res. 14 50 visibility_off ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks              Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen, Yuankai Wu            2024-05-28 ArXiv 0 3 visibility_off Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks              Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang            2020-05-24 Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining 1075 56 visibility_off DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series              Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir            2024-05-29 ArXiv 0 4 visibility_off FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective              Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbin Cao, Zhendong Niu            2023-11-10 ArXiv 46 6 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/","title":"D3dbbd0f0de51b421a6220bd6480b8d2e99a88e9","text":""},{"location":"recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/#_1","title":"D3dbbd0f0de51b421a6220bd6480b8d2e99a88e9","text":"<p> This page was last updated on 2024-11-04 06:05:33 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Graph Time-series Modeling in Deep Learning: A Survey              Hongjie Chen, Hoda Eldardiry            2023-12-23 ACM Transactions on Knowledge Discovery from Data 3 15 visibility_off TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting              Nancy R. Xu, Chrysoula Kosma, M. Vazirgiannis            2023-07-27 ArXiv 1 55 visibility_off Graph Deep Learning for Time Series Forecasting              Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi            2023-10-24 ArXiv 8 50 visibility_off Graph Anomaly Detection in Time Series: A Survey              Thi Kieu Khanh Ho, Ali Karami, N. Armanfard            2023-01-31 ArXiv 2 15 visibility_off Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis              Zhe Liu, Xiang Huang, Jingyun Zhang, Zhifeng Hao, L. Sun, Hao Peng            2024-08-23 ArXiv 0 2 visibility_off FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective              Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbin Cao, Zhendong Niu            2023-11-10 ArXiv 46 6 visibility_off MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series              Wei-Shu Xiong, Xiaochen (Michael) Sun            2022-11-22 ArXiv 1 2 visibility_off Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting              Katrina Chen, M. Feng, T. Wirjanto            2023-02-04 ArXiv 5 22 visibility_off Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection              H. Jo, Seong-Whan Lee            2024-01-25 Inf. Sci. 1 1 visibility_off Graph Neural Alchemist: An innovative fully modular architecture for time series-to-graph classification              Paulo Coelho, Raul Araju, Lu'is Ramos, Samir Saliba, Renato Vimieiro            2024-10-12 ArXiv 0 0 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/","title":"E2a83369383aff37224170c1ae3d3870d5d9e419","text":""},{"location":"recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/#_1","title":"E2a83369383aff37224170c1ae3d3870d5d9e419","text":"<p> This page was last updated on 2024-11-04 06:05:35 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Taming Local Effects in Graph-based Spatiotemporal Forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Graph Deep Learning for Time Series Forecasting              Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi            2023-10-24 ArXiv 8 50 visibility_off Scalable Spatiotemporal Graph Neural Networks              Andrea Cini, Ivan Marisca, F. Bianchi, C. Alippi            2022-09-14 ArXiv 33 50 visibility_off Sparse Graph Learning from Spatiotemporal Time Series              Andrea Cini, Daniele Zambon, C. Alippi            2022-05-26 J. Mach. Learn. Res. 14 50 visibility_off Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling for Social Science              Yifan Li, Yu Lin, Y. Gao, L. Khan            2022-07-18 2022 International Joint Conference on Neural Networks (IJCNN) 0 12 visibility_off ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks              Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen, Yuankai Wu            2024-05-28 ArXiv 0 3 visibility_off FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective              Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbin Cao, Zhendong Niu            2023-11-10 ArXiv 46 6 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/","title":"E2e1f1b8e6c1b7f4f166e15b7c674945856a51b6","text":""},{"location":"recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/#_1","title":"E2e1f1b8e6c1b7f4f166e15b7c674945856a51b6","text":"<p> This page was last updated on 2024-11-04 06:05:42 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters              Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang, Jianling Sun, Chenghao Liu            2024-08-30 ArXiv 0 3 visibility_off Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction              Cheng Feng, Long Huang, Denis Krompass            2024-02-12 ArXiv 4 15 visibility_off Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting              Jiarui Yang, Tao Dai, Naiqi Li, Junxi Wu, Peiyuan Liu, Jinmin Li, Jigang Bao, Haigang Zhang, Shu-Tao Xia            2024-06-04 ArXiv 0 5 visibility_off Chronos: Learning the Language of Time Series              Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang            2024-03-12 ArXiv 63 18 visibility_off Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting              Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilovs, Hena Ghonia, N. Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, I. Rish            2023-10-12 ArXiv 21 40 visibility_off FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting              Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Qingsong Wen, Christian S. Jensen, Bin Yang            2024-10-15 ArXiv 0 27 visibility_off A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model              Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, F. Tsung            2024-05-03 ArXiv 7 47 visibility_off Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning              Yuxuan Bian, Xu Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu            2024-02-07 ArXiv 8 7 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/","title":"E6f0a85009481dcfd93aaa43ed3f980e5033b0d8","text":""},{"location":"recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/#_1","title":"E6f0a85009481dcfd93aaa43ed3f980e5033b0d8","text":"<p> This page was last updated on 2024-11-04 06:06:20 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Learning sparse nonlinear dynamics via mixed-integer optimization Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Automatically discovering ordinary differential equations from data with sparse regression              Kevin Egan, Weizhen Li, Rui Carvalho            2024-01-09 Communications Physics 11 2 visibility_off Discovering governing equations from data by sparse identification of nonlinear dynamical systems              S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences 3383 66 visibility_off Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI)              Gabriel F. Machado, Morgan Jones            2023-10-06 2024 American Control Conference (ACC) 1 1 visibility_off PySINDy: A comprehensive Python package for robust sparse system identification              A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Kathleen P. Champion, Jean-Christophe Loiseau, J. Kutz, S. Brunton            2021-11-12 J. Open Source Softw. 116 66 visibility_off Sparse reconstruction of ordinary differential equations with inference              S. Venkatraman, Sumanta Basu, M. Wells            2023-08-17 ArXiv 0 40 visibility_off Physics-informed learning of governing equations from scarce data              Zhao Chen, Yang Liu, Hao Sun            2020-05-05 Nature Communications 282 13 visibility_off PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data.              Brian M. de Silva, Kathleen P. Champion, M. Quade, Jean-Christophe Loiseau, J. Kutz, S. Brunton            2020-04-17 arXiv: Dynamical Systems 51 66 visibility_off Discovering Interpretable Dynamics by Sparsity Promotion on Energy and the Lagrangian              H. Chu, M. Hayashibe            2020-01-31 IEEE Robotics and Automation Letters 24 24 visibility_off Sparse learning of stochastic dynamical equations.              L. Boninsegna, F. N\u00fcske, C. Clementi            2017-12-06 The Journal of chemical physics 199 46 visibility_off Sparsistent Model Discovery              Georges Tod, G. Both, R. Kusters            2021-06-22 ArXiv 1 13 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/","title":"F45f85fa1beaa795c24c4ff86f1f2deece72252f","text":""},{"location":"recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/#_1","title":"F45f85fa1beaa795c24c4ff86f1f2deece72252f","text":"<p> This page was last updated on 2024-11-04 06:05:39 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article A decoder-only foundation model for time-series forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting              Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilovs, Hena Ghonia, N. 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Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen            2023-10-03 ArXiv 184 9 visibility_off In-context Time Series Predictor              Jiecheng Lu, Yan Sun, Shihao Yang            2024-05-23 ArXiv 2 2 visibility_off FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting              Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Qingsong Wen, Christian S. Jensen, Bin Yang            2024-10-15 ArXiv 0 27 visibility_off LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters              Ching Chang, Wenjie Peng, Tien-Fu Chen            2023-08-16 ArXiv 21 3 visibility_off Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts              X. Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, Ming Jin            2024-09-24 ArXiv 1 9 visibility_off A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model              Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, F. Tsung            2024-05-03 ArXiv 7 47 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/","title":"Ff1f1cf9df8c413ec7345da7604ba28597da5b90","text":""},{"location":"recommendations/ff1f1cf9df8c413ec7345da7604ba28597da5b90/#_1","title":"Ff1f1cf9df8c413ec7345da7604ba28597da5b90","text":"<p> This page was last updated on 2024-11-04 06:05:48 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article UNITS: A Unified Multi-Task Time Series Model Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Timer: Generative Pre-trained Transformers Are Large Time Series Models              Yong Liu, Haoran Zhang, Chenyu Li, Xiangdong Huang, Jianmin Wang, Mingsheng Long            2024-02-04 DBLP, ArXiv 18 67 visibility_off Towards Generalisable Time Series Understanding Across Domains              Ozgun Turgut, Philip Muller, M. Menten, Daniel Rueckert            2024-10-09 ArXiv 0 15 visibility_off Large Pre-trained time series models for cross-domain Time series analysis tasks              Harshavardhan Kamarthi, B. A. Prakash            2023-11-19 ArXiv 4 8 visibility_off TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis              Sabera Talukder, Yisong Yue, Georgia Gkioxari            2024-02-26 ArXiv 6 4 visibility_off Universal Time-Series Representation Learning: A Survey              Patara Trirat, Yooju Shin, Junhyeok Kang, Youngeun Nam, Jihye Na, Minyoung Bae, Joeun Kim, Byunghyun Kim, Jae-Gil Lee            2024-01-08 ArXiv 6 7 visibility_off One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors              Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin            2023-11-24 ArXiv 4 8 visibility_off Chronos: Learning the Language of Time Series              Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang            2024-03-12 ArXiv 63 18 visibility_off Unified Training of Universal Time Series Forecasting Transformers              Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo            2024-02-04 ArXiv 63 25 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"}]}
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+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":"Introduction <p>Welcome to Team VPE's Literature Survey System! This project leverages the powerful Semantic Scholar's Recommendation API to provide you with highly relevant research article recommendations based on your curated lists of articles.</p> <p>Moreover, this website contains curated collection of references on differentiable and learned simulator algorithms for developing digital twins with applications in drug discovery and development.</p> Features <ul> <li>List-based recommendations: For each topic, we use a manually curated list of positive articles. We then select an equal number of negative articles from other topics. Semantics Scholar's recommendation system uses the set of positive and negative to provide up to 300 recommendations per topic.</li> <li>Single-paper recommendations: For each article in our curated lists, Semantics Scholar's recommendation system returns up to 10 related articles.</li> </ul> How it works? <ul> <li>Scroll to the top of this page and click one of the tabs to open the relevant section.</li> <li>Each topic contains two tables. The first table lists the manually curated articles, and the second table lists the articles recommended for that topic by the recommendation system. The last column of the first table contains a hyperlink that opens a new tab with articles recommended for that specific article.</li> <li>Click on the help icon at the top of the page to learn more about that section.</li> </ul>         Check out our GitHub open_in_new repository to learn how to build one for yourself."},{"location":"Symbolic%20regression/","title":"Symbolic regression","text":"<p> This page was last updated on 2024-11-11 06:06:01 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p> </p> <p> </p>"},{"location":"Symbolic%20regression/#manually_curated_articles","title":"Manually curated articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index                View recommendations                visibility_off                  Discovering governing equations from data by sparse identification of nonlinear dynamical systems                               S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences of the United States of America, Proceedings of the National Academy of Sciences 3408 67 open_in_new visibility_off                  Robust learning from noisy, incomplete, high-dimensional experimental data via physically constrained symbolic regression                               Patrick A. K. Reinbold, Logan Kageorge, M. Schatz, R. Grigoriev            2021-02-24 Nature Communications 91 24 open_in_new visibility_off                  Data-driven discovery of coordinates and governing equations                               Kathleen P. Champion, Bethany Lusch, J. Kutz, S. Brunton            2019-03-29 Proceedings of the National Academy of Sciences of the United States of America 648 67 open_in_new visibility_off                  Chaos as an intermittently forced linear system                               S. Brunton, Bingni W. Brunton, J. Proctor, E. Kaiser, J. Kutz            2016-08-18 Nature Communications 468 67 open_in_new visibility_off                  Sparse identification of nonlinear dynamics for model predictive control in the low-data limit                               E. Kaiser, J. Kutz, S. Brunton            2017-11-15 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 461 67 open_in_new visibility_off                  Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics                               N. Mangan, S. Brunton, J. Proctor, J. Kutz            2016-05-26 IEEE Transactions on Molecular, Biological and Multi-Scale Communications, IEEE Transactions on Molecular Biological and Multi-Scale Communications 334 67 open_in_new visibility_off                  SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics                               Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 221 67 open_in_new visibility_off                  Multidimensional Approximation of Nonlinear Dynamical Systems                               Patrick Gel\u00df, Stefan Klus, J. Eisert, Christof Schutte            2018-09-07 Journal of Computational and Nonlinear Dynamics 65 79 open_in_new visibility_off                  Learning Discrepancy Models From Experimental Data                               Kadierdan Kaheman, E. Kaiser, B. Strom, J. Kutz, S. Brunton            2019-09-18 ArXiv, arXiv.org 35 67 open_in_new visibility_off                  Discovery of Physics From Data: Universal Laws and Discrepancies                               Brian M. de Silva, D. Higdon, S. Brunton, J. Kutz            2019-06-19 Frontiers in Artificial Intelligence 77 67 open_in_new visibility_off                  Data-driven discovery of partial differential equations                               S. Rudy, S. Brunton, J. Proctor, J. Kutz            2016-09-21 Science Advances 1235 67 open_in_new visibility_off                  Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control                               Urban Fasel, J. Kutz, Bingni W. Brunton, S. Brunton            2021-11-22 Proceedings of the Royal Society A, Proceedings. Mathematical, Physical, and Engineering Sciences 181 67 open_in_new visibility_off                  Learning sparse nonlinear dynamics via mixed-integer optimization                               D. Bertsimas, Wes Gurnee            2022-06-01 Nonlinear Dynamics 30 92 open_in_new visibility_off                  A Unified Framework for Sparse Relaxed Regularized Regression: SR3                               P. Zheng, T. Askham, S. Brunton, J. Kutz, A. Aravkin            2018-07-14 IEEE Access 124 67 open_in_new Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index View recommendations"},{"location":"Symbolic%20regression/#recommended_articles","title":"Recommended articles on Symbolic regression","text":"Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index visibility_off                LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems                             Haoyang Zheng, Guang Lin            2024-11-04 ArXiv 0 1 visibility_off                ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification                             Siva Viknesh, Younes Tatari, Amirhossein Arzani            2024-10-21 ArXiv 0 24 visibility_off                Deep Generative Modeling for Identification of Noisy, Non-Stationary Dynamical Systems                             Doris Voina, S. Brunton, J. Kutz            2024-10-02 ArXiv 0 67 visibility_off                Weak-form modified sparse identification of nonlinear dynamics                             Cristian L'opez, 'Angel Naranjo, Diego Salazar, Keegan J. Moore            2024-10-23 ArXiv 0 1 visibility_off                Data-driven model discovery and model selection for noisy biological systems                             Xiaojun Wu, MeiLu McDermott, Adam L. Maclean            2024-10-04 bioRxiv 0 19 visibility_off                Physics-informed AI and ML-based sparse system identification algorithm for discovery of PDE's representing nonlinear dynamic systems                             A. Pal, Sutanu Bhowmick, Satish Nagarajaiah            2024-10-13 ArXiv 0 9 visibility_off                Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems                             Florian Wolf, Nicol\u00f2 Botteghi, Urban Fasel, Andrea Manzoni            2024-11-06 ArXiv 0 11 visibility_off                Parsimonious Dynamic Mode Decomposition: A Robust and Automated Approach for Optimally Sparse Mode Selection in Complex Systems                             Arpan Das, Pier Marzocca, O. Levinski            2024-10-22 ArXiv 0 9 visibility_off                Governing equation discovery of a complex system from snapshots                             Qunxi Zhu, Bolin Zhao, Jingdong Zhang, Peiyang Li, Wei Lin            2024-10-22 ArXiv 0 10 visibility_off                Automated Global Analysis of Experimental Dynamics through Low-Dimensional Linear Embeddings                             Samuel A. Moore, Brian P. Mann, Boyuan Chen            2024-11-01 ArXiv 0 2 visibility_off                Discovering Governing Equations of Biological Systems through Representation Learning and Sparse Model Discovery                             Mehrshad Sadria, Vasu Swaroop            2024-09-23 bioRxiv 0 8 visibility_off                Data-driven prediction of large-scale spatiotemporal chaos with distributed low-dimensional models                             Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham            2024-10-02 ArXiv 0 8 visibility_off                Online learning in bifurcating dynamic systems via SINDy and Kalman filtering                             Luca Rosafalco, Paolo Conti, Andrea Manzoni, Stefano Mariani, A. Frangi            2024-11-07 ArXiv 0 8 visibility_off                Data-driven model discovery with Kolmogorov-Arnold networks                             Mohammadamin Moradi, Shirin Panahi, E. Bollt, Ying-Cheng Lai            2024-09-23 ArXiv 2 37 visibility_off                An evolutionary approach for discovering non-Gaussian stochastic dynamical systems based on nonlocal Kramers-Moyal formulas                             Yang Li, Shengyuan Xu, Jinqiao Duan            2024-09-29 ArXiv 0 0 visibility_off                Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical Systems                             Kan Li, Jos'e C. Pr'incipe            2024-10-31 ArXiv 0 2 visibility_off                CGKN: A Deep Learning Framework for Modeling Complex Dynamical Systems and Efficient Data Assimilation                             Chuanqi Chen, Nan Chen, Yinling Zhang, Jin-Long Wu            2024-10-26 ArXiv 0 3 visibility_off                Discovery and inversion of the viscoelastic wave equation in inhomogeneous media                             Su Chen, Yi Ding, Hiroe Miyake, Xiaojun Li            2024-09-27 ArXiv 0 2 visibility_off                Fourier Domain Physics Informed Neural Network                             Jonathan Musgrave, Shu-Wei Huang            2024-09-30 ArXiv 0 1 visibility_off                Reinforcement learning-based estimation for spatio-temporal systems                             Saviz Mowlavi, M. Benosman            2024-09-28 Scientific Reports 0 23 visibility_off                Automated Discovery of Continuous Dynamics from Videos                             Kuang Huang, Dong Heon Cho, Boyuan Chen            2024-10-14 ArXiv 1 0 visibility_off                Stability analysis of chaotic systems in latent spaces                             Elise Ozalp, L. Magri            2024-10-01 ArXiv 0 2 visibility_off                Global dynamical structures from infinitesimal data                             Benjamin W. McInroe, R. Full, D. Koditschek, Yuliy M. Baryshnikov            2024-10-03 ArXiv 0 68 visibility_off                Parametric Taylor series based latent dynamics identification neural networks                             Xinlei Lin, Dunhui Xiao            2024-10-05 ArXiv 0 0 visibility_off                Response Estimation and System Identification of Dynamical Systems via Physics-Informed Neural Networks                             M. Haywood-Alexander, Giacamo Arcieri, A. Kamariotis, Eleni Chatzi            2024-10-02 ArXiv 0 4 visibility_off                Reconstructing dynamics from sparse observations with no training on target system                             Zheng-Meng Zhai, Jun-Yin Huang, Benjamin D. Stern, Ying-Cheng Lai            2024-10-28 ArXiv 0 5 visibility_off                Measure-Theoretic Time-Delay Embedding                             Jonah Botvinick-Greenhouse, Maria Oprea, R. Maulik, Yunan Yang            2024-09-13 ArXiv 0 22 visibility_off                Machine learning sparse reaction-diffusion models from stochastic dynamics and spatiotemporal patterns                             Bedri Abubaker-Sharif, Peter N. Devreotes, Pablo A. Iglesias            2024-10-03 bioRxiv 0 4 visibility_off                Modeling chaotic Lorenz ODE System using Scientific Machine Learning                             Sameera S Kashyap, R. Dandekar, R. Dandekar, S. Panat            2024-10-09 ArXiv 0 3 visibility_off                Data-driven discovery of chemotactic migration of bacteria via coordinate-invariant machine learning                             Y. M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee, S. Datta, J. M. Bello-Rivas, Ioannis G Kevrekidis            2024-10-24 BMC Bioinformatics 0 35 visibility_off                Physics-informed Kolmogorov-Arnold Network with Chebyshev Polynomials for Fluid Mechanics                             Chunyu Guo, Lucheng Sun, Shilong Li, Zelong Yuan, Chao Wang            2024-11-07 ArXiv 0 1 visibility_off                Data-Driven Discovery of Conservation Laws from Trajectories via Neural Deflation                             Shaoxuan Chen, Panayotis Kevrekidis, Hong-Kun Zhang, Wei Zhu            2024-10-07 ArXiv 0 1 visibility_off                Estimate of Koopman modes and eigenvalues with Kalman Filter                             Ningxin Liu, Shuigen Liu, Xin T. Tong, Lijian Jiang            2024-09-24 ArXiv 0 2 visibility_off                Causal Discovery in Nonlinear Dynamical Systems using Koopman Operators                             Adam Rupe, Derek DeSantis, Craig Bakker, Parvathi Kooloth, Jian Lu            2024-10-14 ArXiv 0 2 visibility_off                Physics-aligned Schr\u00f6dinger bridge                             Zeyu Li, Hongkun Dou, Shen Fang, Wang Han, Yue Deng, Lijun Yang            2024-09-26 ArXiv 0 2 visibility_off                A method for identifying causality in the response of nonlinear dynamical systems                             Joseph Massingham, Ole Nielsen, Tore Butlin            2024-09-26 ArXiv 0 1 visibility_off                Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction                             Manuel Brenner, Christoph Jurgen Hemmer, Z. Monfared, Daniel Durstewitz            2024-10-18 ArXiv 0 7 visibility_off                Learning Generalized Hamiltonians using fully Symplectic Mappings                             Harsh Choudhary, Chandan Gupta, Vyacheslav kungrutsev, Melvin Leok, Georgios Korpas            2024-09-17 ArXiv 0 0 visibility_off                Learning Chaotic Dynamics with Embedded Dissipativity                             Sunbochen Tang, T. Sapsis, Navid Azizan            2024-10-01 ArXiv 0 39 visibility_off                Informative Input Design for Dynamic Mode Decomposition                             Joshua Ott, Mykel J. Kochenderfer, Stephen Boyd            2024-09-19 ArXiv 0 17 visibility_off                Barycentric rational approximation for learning the index of a dynamical system from limited data                             Davide Pradovera, I. V. Gosea, Jan Heiland            2024-10-02 ArXiv 0 12 visibility_off                Gaussian Processes simplify differential equations                             Jonghyeon Lee, B. Hamzi, Y. Kevrekidis, H. Owhadi            2024-10-03 ArXiv 0 36 visibility_off                Bridging the Gap between Koopmanism and Response Theory: Using Natural Variability to Predict Forced Response                             Niccol\u00f2 Zagli, Matthew Colbrook, Valerio Lucarini, Igor Mezi'c, John Moroney            2024-10-02 ArXiv 0 5 visibility_off                Efficient pseudometrics for data-driven comparisons of nonlinear dynamical systems                             Bryan Glaz            2024-09-27 ArXiv 0 0 visibility_off                Koopman Spectral Analysis from Noisy Measurements based on Bayesian Learning and Kalman Smoothing                             Zhexuan Zeng, Jun Zhou, Yasen Wang, Zuowei Ping            2024-10-01 ArXiv 0 0 visibility_off                Physics-Informed Echo State Networks for Modeling Controllable Dynamical Systems                             Eric Mochiutti, Eric A. Antonelo, Eduardo Camponogara            2024-09-27 ArXiv 0 14 visibility_off                Discovery of Quasi-Integrable Equations from traveling-wave data using the Physics-Informed Neural Networks                             A. Nakamula, N. Sawado, K. Shimasaki, Y. Shimazaki, Y. Suzuki, K. Toda            2024-10-23 ArXiv 0 1 visibility_off                Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains                             Razmik Arman Khosrovian, Takaharu Yaguchi, Hiroaki Yoshimura, Takashi Matsubara            2024-10-15 ArXiv 0 11 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index"},{"location":"Time-series%20forecasting/","title":"Time-series forecasting","text":"<p> This page was last updated on 2024-11-11 06:05:40 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p> </p> <p> </p>"},{"location":"Time-series%20forecasting/#manually_curated_articles","title":"Manually curated articles on Time-series forecasting","text":"Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index                View recommendations                visibility_off                  A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection                               Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan            2023-07-07 IEEE Transactions on Pattern Analysis and Machine Intelligence 82 50 open_in_new visibility_off                  Graph-Guided Network for Irregularly Sampled Multivariate Time Series                               Xiang Zhang, M. Zeman, Theodoros Tsiligkaridis, M. Zitnik            2021-10-11 ArXiv, International Conference on Learning Representations 86 47 open_in_new visibility_off                  Taming Local Effects in Graph-based Spatiotemporal Forecasting                               Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi            2023-02-08 ArXiv, Neural Information Processing Systems 22 50 open_in_new visibility_off                  Sparse Graph Learning from Spatiotemporal Time Series                               Andrea Cini, Daniele Zambon, C. Alippi            2022-05-26 J. Mach. Learn. Res., Journal of machine learning research 14 50 open_in_new visibility_off                  Graph Deep Learning for Time Series Forecasting                               Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi            2023-10-24 ArXiv, arXiv.org 9 50 open_in_new visibility_off                  Large Language Models Are Zero-Shot Time Series Forecasters                               Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson            2023-10-11 ArXiv, Neural Information Processing Systems 198 14 open_in_new visibility_off                  Graph-Mamba: Towards Long-Range Graph Sequence Modeling with Selective State Spaces                               Chloe X. Wang, Oleksii Tsepa, Jun Ma, Bo Wang            2024-02-01 ArXiv, arXiv.org 56 5 open_in_new visibility_off                  A decoder-only foundation model for time-series forecasting                               Abhimanyu Das, Weihao Kong, Rajat Sen, Yichen Zhou            2023-10-14 ArXiv, International Conference on Machine Learning 103 14 open_in_new visibility_off                  Unified Training of Universal Time Series Forecasting Transformers                               Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong, Silvio Savarese, Doyen Sahoo            2024-02-04 ArXiv, International Conference on Machine Learning 71 25 open_in_new visibility_off                  Time-LLM: Time Series Forecasting by Reprogramming Large Language Models                               Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, X. Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen            2023-10-03 ArXiv, International Conference on Learning Representations 195 9 open_in_new visibility_off                  Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series                               Vijay Ekambaram, Arindam Jati, Nam H. Nguyen, Pankaj Dayama, Chandra Reddy, Wesley M. Gifford, Jayant Kalagnanam            2024-01-08 ArXiv, arXiv.org 3 3 open_in_new visibility_off                  Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency                               Xiang Zhang, Ziyuan Zhao, Theodoros Tsiligkaridis, M. Zitnik            2022-06-17 ArXiv, Neural Information Processing Systems 211 47 open_in_new visibility_off                  Domain Adaptation for Time Series Under Feature and Label Shifts                               Huan He, Owen Queen, Teddy Koker, Consuelo Cuevas, Theodoros Tsiligkaridis, M. Zitnik            2023-02-06 ArXiv, DBLP 38 47 open_in_new visibility_off                  AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs                               Daniele Zambon, C. Alippi            None DBLP 6 50 open_in_new visibility_off                  Graph state-space models                               Daniele Zambon, Andrea Cini, L. Livi, C. Alippi            2023-01-04 ArXiv, arXiv.org 4 50 open_in_new visibility_off                  UNITS: A Unified Multi-Task Time Series Model                               Shanghua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen, Theodoros Tsiligkaridis, M. Zitnik            2024-02-29 ArXiv 4 47 open_in_new Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index View recommendations"},{"location":"Time-series%20forecasting/#recommended_articles","title":"Recommended articles on Time-series forecasting","text":"Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index visibility_off                A Mamba Foundation Model for Time Series Forecasting                             Haoyu Ma, Yushu Chen, Wenlai Zhao, Jinzhe Yang, Yingsheng Ji, Xinghua Xu, Xiaozhu Liu, Hao Jing, Shengzhuo Liu, Guangwen Yang            2024-11-05 ArXiv 0 7 visibility_off                Metadata Matters for Time Series: Informative Forecasting with Transformers                             Jiaxiang Dong, Haixu Wu, Yuxuan Wang, Li Zhang, Jianmin Wang, Mingsheng Long            2024-10-04 ArXiv 0 67 visibility_off                FlexTSF: A Universal Forecasting Model for Time Series with Variable Regularities                             Jingge Xiao, Yile Chen, Gao Cong, Wolfgang Nejdl, Simon Gottschalk            2024-10-30 ArXiv 0 6 visibility_off                Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts                             X. Shi, Shiyu Wang, Yuqi Nie, Dianqi Li, Zhou Ye, Qingsong Wen, Ming Jin            2024-09-24 ArXiv 2 9 visibility_off                Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models                             Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien N. Siems, David Salinas, Frank Hutter            2024-10-12 ArXiv 0 8 visibility_off                Timer-XL: Long-Context Transformers for Unified Time Series Forecasting                             Yong Liu, Guo Qin, Xiangdong Huang, Jianmin Wang, Mingsheng Long            2024-10-07 ArXiv 0 67 visibility_off                xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories                             Maurice Kraus, Felix Divo, D. Dhami, K. Kersting            2024-10-22 ArXiv 0 12 visibility_off                In-Context Fine-Tuning for Time-Series Foundation Models                             Abhimanyu Das, Matthew Faw, Rajat Sen, Yichen Zhou            2024-10-31 ArXiv 0 14 visibility_off                Towards Long-Context Time Series Foundation Models                             Nina Zukowska, Mononito Goswami, Micha\u0142 Wili\u0144ski, Willa Potosnak, Artur Dubrawski            2024-09-20 ArXiv 0 8 visibility_off                Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts                             Xu Liu, Juncheng Liu, Gerald Woo, Taha \u0130brahim Aksu, Yuxuan Liang, Roger Zimmermann, Chenghao Liu, Silvio Savarese, Caiming Xiong, Doyen Sahoo            2024-10-14 ArXiv 0 25 visibility_off                LLM-Mixer: Multiscale Mixing in LLMs for Time Series Forecasting                             Md. Kowsher, Md. Shohanur, Islam Sobuj, Nusrat Jahan Prottasha, E. A. Alanis, O. Garibay, Niloofar Yousefi, Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth\u00e9e Lacroix, Baptiste Rozi\u00e8re, Naman Goyal, Eric Hambro, Xuan Zhang, Xun Liang, Aakas Zhiyuli, Shusen Zhang, Rui Xu, Bo Wu. 2019, Huaqin Zhao, Zheng Liu, Zihao Wu, Yiwei Li, Tianze Yang, Peng Shu, Shaochen Xu, Haixing Dai, Lin Zhao, Gengchen Mai, Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wan Zhang            2024-10-15 ArXiv 0 34 visibility_off                Diffusion Auto-regressive Transformer for Effective Self-supervised Time Series Forecasting                             Daoyu Wang, Mingyue Cheng, Zhiding Liu, Qi Liu, Enhong Chen            2024-10-08 ArXiv 1 11 visibility_off                On the Regularization of Learnable Embeddings for Time Series Processing                             L. Butera, G. Felice, Andrea Cini, C. Alippi            2024-10-18 ArXiv 0 50 visibility_off                PSformer: Parameter-efficient Transformer with Segment Attention for Time Series Forecasting                             Yanlong Wang, Jian Xu, Fei Ma, Shao-Lun Huang, Danny D. Sun, Xiao-Ping Zhang            2024-11-03 ArXiv 0 1 visibility_off                Revisited Large Language Model for Time Series Analysis through Modality Alignment                             L. Zheng, C. Dong, W. 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Sundaram            2023-02-08 ArXiv 7 37 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/883547fdbd88552328a6615ec620f96e39c57018/","title":"883547fdbd88552328a6615ec620f96e39c57018","text":""},{"location":"recommendations/883547fdbd88552328a6615ec620f96e39c57018/#_1","title":"883547fdbd88552328a6615ec620f96e39c57018","text":"<p> This page was last updated on 2024-11-11 06:05:51 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Sparsifying priors for Bayesian uncertainty quantification in model discovery              Seth M. Hirsh, D. Barajas-Solano, J. Kutz            2021-07-05 Royal Society Open Science 61 32 visibility_off Convergence of uncertainty estimates in Ensemble and Bayesian sparse model discovery              Liyao (Mars) Gao, Urban Fasel, S. Brunton, J. Kutz            2023-01-30 ArXiv 11 67 visibility_off Automatically discovering ordinary differential equations from data with sparse regression              Kevin Egan, Weizhen Li, Rui Carvalho            2024-01-09 Communications Physics 11 2 visibility_off SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics              Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings. Mathematical, Physical, and Engineering Sciences 221 67 visibility_off Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data              Lloyd Fung, Urban Fasel, M. Juniper            2024-02-23 ArXiv 1 38 visibility_off Discovering governing equations from data by sparse identification of nonlinear dynamical systems              S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences 3408 67 visibility_off Sparse identification of nonlinear dynamics in the presence of library and system uncertainty              Andrew O'Brien            2024-01-23 ArXiv 0 0 visibility_off Comparative Analysis of Uncertainty Quantification Models in Active Learning for Efficient System Identification of Dynamical Systems              Hans Mertens, Frances Zhu            2024-08-28 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE) 0 0 $\\dot { \\boldsymbol x} = { \\boldsymbol f} ({ \\boldsymbol x})$ . First, we propose, for use in high-noise settings, an extensive toolkit of critically enabling extensions for the SINDy regression method, to progressively cull functionals from an over-complete library and yield a set of sparse equations that regress to the derivate  $\\dot { \\boldsymbol {x}}$ . This toolkit includes: (regression step) weight timepoints based on estimated noise, use ensembles to estimate coefficients, and regress using FFTs; (culling step) leverage linear dependence of functionals, and restore and protect culled functionals based on Figures of Merit (FoMs). In a novel Assessment step, we define FoMs that compare model predictions to the original time-series (i.e.,  ${ \\boldsymbol x}(t)$  rather than  $\\dot { \\boldsymbol {x}}(t)$ ). These innovations can extract sparse governing equations and coefficients from high-noise time-series data (e.g., 300% added noise). For example, it discovers the correct sparse libraries in the Lorenz system, with median coefficient estimate errors equal to 1%\u22123% (for 50% noise), 6%\u22128% (for 100% noise), and 23%\u221225% (for 300% noise). The enabling modules in the toolkit are combined into a single method, but the individual modules can be tactically applied in other equation discovery methods (SINDy or not) to improve results on high-noise data. Second, we propose a technique, applicable to any model discovery method based on  $\\dot { \\boldsymbol x} = { \\boldsymbol f} ({ \\boldsymbol x})$ , to assess the accuracy of a discovered model in the context of non-unique solutions due to noisy data. Currently, this non-uniqueness can obscure a discovered model\u2019s accuracy and thus a discovery method\u2019s effectiveness. We describe a technique that uses linear dependencies among functionals to transform a discovered model into an equivalent form that is closest to the true model, enabling more accurate assessment of a discovered model\u2019s correctness.\"&gt;           visibility_off A Toolkit for Data-Driven Discovery of Governing Equations in High-Noise Regimes              Charles B. Delahunt, J. Kutz            2021-11-08 IEEE Access 16 32 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/","title":"B2eb064f432557c59ce99834d7dc7817e4687271","text":""},{"location":"recommendations/b2eb064f432557c59ce99834d7dc7817e4687271/#_1","title":"B2eb064f432557c59ce99834d7dc7817e4687271","text":"<p> This page was last updated on 2024-11-11 06:05:43 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Sparse identification of nonlinear dynamics for model predictive control in the low-data limit Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off SINDy vs Hard Nonlinearities and Hidden Dynamics: a Benchmarking Study              Aurelio Raffa Ugolini, Valentina Breschi, Andrea Manzoni, M. Tanelli            2024-03-01 ArXiv 1 2 visibility_off Discovering Interpretable Dynamics by Sparsity Promotion on Energy and the Lagrangian              H. Chu, M. Hayashibe            2020-01-31 IEEE Robotics and Automation Letters 24 24 visibility_off SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics              Kadierdan Kaheman, J. Kutz, S. Brunton            2020-04-05 Proceedings. Mathematical, Physical, and Engineering Sciences 221 67 visibility_off Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI)              Gabriel F. Machado, Morgan Jones            2023-10-06 2024 American Control Conference (ACC) 1 1 visibility_off Multi-objective SINDy for parameterized model discovery from single transient trajectory data              Javier A. Lemus, Benjamin Herrmann            2024-05-14 ArXiv 0 0 visibility_off Discovering governing equations from data by sparse identification of nonlinear dynamical systems              S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences 3408 67 visibility_off PySINDy: A comprehensive Python package for robust sparse system identification              A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Kathleen P. Champion, Jean-Christophe Loiseau, J. Kutz, S. Brunton            2021-11-12 J. Open Source Softw. 119 67 visibility_off ADAM-SINDy: An Efficient Optimization Framework for Parameterized Nonlinear Dynamical System Identification              Siva Viknesh, Younes Tatari, Amirhossein Arzani            2024-10-21 ArXiv 0 24 visibility_off Nonlinear Control of Networked Dynamical Systems              Megan Morrison, Nathan Kutz            2020-06-09 IEEE Transactions on Network Science and Engineering 10 5 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/","title":"C0fc3882a9976f6a9cdc3a724bce184b786503da","text":""},{"location":"recommendations/c0fc3882a9976f6a9cdc3a724bce184b786503da/#_1","title":"C0fc3882a9976f6a9cdc3a724bce184b786503da","text":"<p> This page was last updated on 2024-11-11 06:05:54 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article A Unified Framework for Sparse Relaxed Regularized Regression: SR3 Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Sparse Relaxed Regularized Regression: SR3              P. Zheng, T. Askham, S. Brunton, J. Kutz, A. Aravkin            2018-07-14 ArXiv 9 67 visibility_off Rank-one Convexification for Sparse Regression              Alper Atamt\u00fcrk, A. G\u00f3mez            2019-01-29 ArXiv 48 36 visibility_off Sparse Recovery via Partial Regularization: Models, Theory and Algorithms              Zhaosong Lu, Xiaorui Li            2015-11-23 ArXiv 37 32 visibility_off Structured Regularizers for High-Dimensional Problems: Statistical and Computational Issues              M. Wainwright            2014-01-03 59 95 visibility_off Compressed Sparse Linear Regression              S. Kasiviswanathan, M. Rudelson            2017-07-25 ArXiv 1 30 visibility_off WARPd: A linearly convergent first-order method for inverse problems with approximate sharpness conditions              Matthew J. Colbrook            2021-10-24 ArXiv 2 17 $\\ell _1$ -norm as the loss function for the residual error and utilizes a generalized nonconvex penalty for sparsity inducing. The $\\ell _1$ -loss is less sensitive to outliers in the measurements than the popular  $\\ell _2$-loss, while the nonconvex penalty has the capability of ameliorating the bias problem of the popular convex LASSO penalty and thus can yield more accurate recovery. To solve this nonconvex and nonsmooth minimization formulation efficiently, we propose a first-order algorithm based on alternating direction method of multipliers. A smoothing strategy on the $\\ell _1$ -loss function has been used in deriving the new algorithm to make it convergent. Further, a sufficient condition for the convergence of the new algorithm has been provided for generalized nonconvex regularization. In comparison with several state-of-the-art algorithms, the new algorithm showed better performance in numerical experiments in recovering sparse signals and compressible images. The new algorithm scales well for large-scale problems, as often encountered in image processing.\"&gt;           visibility_off Efficient and Robust Recovery of Sparse Signal and Image Using Generalized Nonconvex Regularization              Fei Wen, L. Pei, Yuan Yang, Wenxian Yu, Peilin Liu            2017-03-23 IEEE Transactions on Computational Imaging 87 31 visibility_off Regularizers for structured sparsity              C. Micchelli, Jean Morales, M. Pontil            2010-10-04 Advances in Computational Mathematics 80 70 visibility_off Regularizers for structured sparsity              C. Micchelli, Jean Morales, M. Pontil            2010-10-04 Advances in Computational Mathematics 80 70 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/","title":"C3c94ccc094dcf546e8e31c9a42506302e837524","text":""},{"location":"recommendations/c3c94ccc094dcf546e8e31c9a42506302e837524/#_1","title":"C3c94ccc094dcf546e8e31c9a42506302e837524","text":"<p> This page was last updated on 2024-11-11 06:05:25 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off AZ-whiteness test: a test for uncorrelated noise on spatio-temporal graphs              Daniele Zambon, C. Alippi            2022-04-23 ArXiv 7 50 visibility_off A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection              Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, C. Alippi, G. I. Webb, Irwin King, Shirui Pan            2023-07-07 IEEE Transactions on Pattern Analysis and Machine Intelligence 82 50 visibility_off Learning Time-Aware Graph Structures for Spatially Correlated Time Series Forecasting              Minbo Ma, Jilin Hu, Christian S. Jensen, Fei Teng, Peng Han, Zhiqiang Xu, Tian-Jie Li            2023-12-27 2024 IEEE 40th International Conference on Data Engineering (ICDE) 0 13 visibility_off Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches              Stefan Bloemheuvel, J. 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Yang, Shirui Pan            2022-02-17 IEEE Transactions on Knowledge and Data Engineering 69 44 visibility_off Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection              Yuyin Tian, Rong Gao, Lingyu Yan, Donghua Liu, Zhiwei Ye            2023-09-07 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) 0 4 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/","title":"Ccea298edb788edf821aef58f0952c3e8debc25a","text":""},{"location":"recommendations/ccea298edb788edf821aef58f0952c3e8debc25a/#_1","title":"Ccea298edb788edf821aef58f0952c3e8debc25a","text":"<p> This page was last updated on 2024-11-11 06:05:11 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Graph Deep Learning for Time Series Forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting              Nancy R. Xu, Chrysoula Kosma, M. Vazirgiannis            2023-07-27 ArXiv 1 55 visibility_off Balanced Graph Structure Learning for Multivariate Time Series Forecasting              Weijun Chen, Yanze Wang, Chengshuo Du, Zhenglong Jia, Feng Liu, Ran Chen            2022-01-24 ArXiv 0 11 visibility_off Sparse Graph Learning from Spatiotemporal Time Series              Andrea Cini, Daniele Zambon, C. Alippi            2022-05-26 J. Mach. Learn. Res. 14 50 visibility_off ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks              Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen, Yuankai Wu            2024-05-28 ArXiv 0 3 visibility_off Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks              Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang            2020-05-24 Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining 1082 57 visibility_off DeepHGNN: Study of Graph Neural Network based Forecasting Methods for Hierarchically Related Multivariate Time Series              Abishek Sriramulu, Nicolas Fourrier, Christoph Bergmeir            2024-05-29 ArXiv 0 4 visibility_off FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective              Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbin Cao, Zhendong Niu            2023-11-10 ArXiv 49 6 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/","title":"D3dbbd0f0de51b421a6220bd6480b8d2e99a88e9","text":""},{"location":"recommendations/d3dbbd0f0de51b421a6220bd6480b8d2e99a88e9/#_1","title":"D3dbbd0f0de51b421a6220bd6480b8d2e99a88e9","text":"<p> This page was last updated on 2024-11-11 06:05:07 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Graph Time-series Modeling in Deep Learning: A Survey              Hongjie Chen, Hoda Eldardiry            2023-12-23 ACM Transactions on Knowledge Discovery from Data 3 15 visibility_off A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification              Flavio Corradini, Marco Gori, Carlo Lucheroni, Marco Piangerelli, Martina Zannotti            2024-10-29 ArXiv 0 12 visibility_off TimeGNN: Temporal Dynamic Graph Learning for Time Series Forecasting              Nancy R. Xu, Chrysoula Kosma, M. Vazirgiannis            2023-07-27 ArXiv 1 55 visibility_off Graph Deep Learning for Time Series Forecasting              Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi            2023-10-24 ArXiv 9 50 visibility_off Graph Anomaly Detection in Time Series: A Survey              Thi Kieu Khanh Ho, Ali Karami, N. Armanfard            2023-01-31 ArXiv 2 15 visibility_off Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis              Zhe Liu, Xiang Huang, Jingyun Zhang, Zhifeng Hao, L. Sun, Hao Peng            2024-08-23 ArXiv 0 2 visibility_off FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective              Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbin Cao, Zhendong Niu            2023-11-10 ArXiv 49 6 visibility_off MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series              Wei-Shu Xiong, Xiaochen (Michael) Sun            2022-11-22 ArXiv 1 2 visibility_off Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting              Katrina Chen, M. Feng, T. Wirjanto            2023-02-04 ArXiv 5 22 visibility_off Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection              H. Jo, Seong-Whan Lee            2024-01-25 Inf. Sci. 1 1 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/","title":"E2a83369383aff37224170c1ae3d3870d5d9e419","text":""},{"location":"recommendations/e2a83369383aff37224170c1ae3d3870d5d9e419/#_1","title":"E2a83369383aff37224170c1ae3d3870d5d9e419","text":"<p> This page was last updated on 2024-11-11 06:05:10 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Taming Local Effects in Graph-based Spatiotemporal Forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Graph Deep Learning for Time Series Forecasting              Andrea Cini, Ivan Marisca, Daniele Zambon, C. Alippi            2023-10-24 ArXiv 9 50 visibility_off Scalable Spatiotemporal Graph Neural Networks              Andrea Cini, Ivan Marisca, F. Bianchi, C. Alippi            2022-09-14 ArXiv 34 50 visibility_off Sparse Graph Learning from Spatiotemporal Time Series              Andrea Cini, Daniele Zambon, C. Alippi            2022-05-26 J. Mach. Learn. Res. 14 50 visibility_off Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling for Social Science              Yifan Li, Yu Lin, Y. Gao, L. Khan            2022-07-18 2022 International Joint Conference on Neural Networks (IJCNN) 0 12 visibility_off ForecastGrapher: Redefining Multivariate Time Series Forecasting with Graph Neural Networks              Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen, Yuankai Wu            2024-05-28 ArXiv 0 3 visibility_off FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective              Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbin Cao, Zhendong Niu            2023-11-10 ArXiv 49 6 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/","title":"E2e1f1b8e6c1b7f4f166e15b7c674945856a51b6","text":""},{"location":"recommendations/e2e1f1b8e6c1b7f4f166e15b7c674945856a51b6/#_1","title":"E2e1f1b8e6c1b7f4f166e15b7c674945856a51b6","text":"<p> This page was last updated on 2024-11-11 06:05:19 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off VisionTS: Visual Masked Autoencoders Are Free-Lunch Zero-Shot Time Series Forecasters              Mouxiang Chen, Lefei Shen, Zhuo Li, Xiaoyun Joy Wang, Jianling Sun, Chenghao Liu            2024-08-30 ArXiv 0 3 visibility_off Generative Pre-Trained Diffusion Paradigm for Zero-Shot Time Series Forecasting              Jiarui Yang, Tao Dai, Naiqi Li, Junxi Wu, Peiyuan Liu, Jinmin Li, Jigang Bao, Haigang Zhang, Shu-Tao Xia            2024-06-04 ArXiv 0 5 visibility_off Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction              Cheng Feng, Long Huang, Denis Krompass            2024-02-12 ArXiv 4 15 visibility_off Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting              Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilovs, Hena Ghonia, N. Hassen, Anderson Schneider, Sahil Garg, Alexandre Drouin, Nicolas Chapados, Yuriy Nevmyvaka, I. Rish            2023-10-12 ArXiv 23 40 visibility_off Chronos: Learning the Language of Time Series              Abdul Fatir Ansari, Lorenzo Stella, Caner Turkmen, Xiyuan Zhang, Pedro Mercado, Huibin Shen, Oleksandr Shchur, Syama Sundar Rangapuram, Sebastian Pineda Arango, Shubham Kapoor, Jasper Zschiegner, Danielle C. Maddix, Michael W. Mahoney, Kari Torkkola, Andrew Gordon Wilson, Michael Bohlke-Schneider, Yuyang Wang            2024-03-12 ArXiv 69 18 visibility_off FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting              Zhe Li, Xiangfei Qiu, Peng Chen, Yihang Wang, Hanyin Cheng, Yang Shu, Jilin Hu, Chenjuan Guo, Aoying Zhou, Qingsong Wen, Christian S. Jensen, Bin Yang            2024-10-15 ArXiv 0 27 visibility_off A Survey of Time Series Foundation Models: Generalizing Time Series Representation with Large Language Model              Jiexia Ye, Weiqi Zhang, Ke Yi, Yongzi Yu, Ziyue Li, Jia Li, F. Tsung            2024-05-03 ArXiv 7 47 visibility_off Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning              Yuxuan Bian, Xu Ju, Jiangtong Li, Zhijian Xu, Dawei Cheng, Qiang Xu            2024-02-07 ArXiv 8 8 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/","title":"E6f0a85009481dcfd93aaa43ed3f980e5033b0d8","text":""},{"location":"recommendations/e6f0a85009481dcfd93aaa43ed3f980e5033b0d8/#_1","title":"E6f0a85009481dcfd93aaa43ed3f980e5033b0d8","text":"<p> This page was last updated on 2024-11-11 06:05:53 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article Learning sparse nonlinear dynamics via mixed-integer optimization Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Automatically discovering ordinary differential equations from data with sparse regression              Kevin Egan, Weizhen Li, Rui Carvalho            2024-01-09 Communications Physics 11 2 visibility_off Discovering governing equations from data by sparse identification of nonlinear dynamical systems              S. Brunton, J. Proctor, J. Kutz            2015-09-11 Proceedings of the National Academy of Sciences 3408 67 visibility_off Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI)              Gabriel F. Machado, Morgan Jones            2023-10-06 2024 American Control Conference (ACC) 1 1 visibility_off PySINDy: A comprehensive Python package for robust sparse system identification              A. Kaptanoglu, Brian M. de Silva, Urban Fasel, Kadierdan Kaheman, Jared L. Callaham, Charles B. Delahunt, Kathleen P. Champion, Jean-Christophe Loiseau, J. Kutz, S. Brunton            2021-11-12 J. Open Source Softw. 119 67 visibility_off Sparse reconstruction of ordinary differential equations with inference              S. Venkatraman, Sumanta Basu, M. Wells            2023-08-17 ArXiv 0 40 visibility_off Physics-informed learning of governing equations from scarce data              Zhao Chen, Yang Liu, Hao Sun            2020-05-05 Nature Communications 289 13 visibility_off PySINDy: A Python package for the Sparse Identification of Nonlinear Dynamics from Data.              Brian M. de Silva, Kathleen P. Champion, M. Quade, Jean-Christophe Loiseau, J. Kutz, S. Brunton            2020-04-17 arXiv: Dynamical Systems 51 67 visibility_off Discovering Interpretable Dynamics by Sparsity Promotion on Energy and the Lagrangian              H. Chu, M. Hayashibe            2020-01-31 IEEE Robotics and Automation Letters 24 24 visibility_off Sparse learning of stochastic dynamical equations.              L. Boninsegna, F. N\u00fcske, C. Clementi            2017-12-06 The Journal of chemical physics 200 46 Abstract Title Authors Publication Date Journal/Conference Citation count Highest h-index </p>"},{"location":"recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/","title":"F45f85fa1beaa795c24c4ff86f1f2deece72252f","text":""},{"location":"recommendations/f45f85fa1beaa795c24c4ff86f1f2deece72252f/#_1","title":"F45f85fa1beaa795c24c4ff86f1f2deece72252f","text":"<p> This page was last updated on 2024-11-11 06:05:15 UTC </p> <p>          Click here for a quick intro of the page! help </p> <p>      Recommendations for the article A decoder-only foundation model for time-series forecasting Abstract Title Authors Publication Date Journal/ Conference Citation count Highest h-index visibility_off Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting              Kashif Rasul, Arjun Ashok, Andrew Robert Williams, Arian Khorasani, George Adamopoulos, Rishika Bhagwatkar, Marin Bilovs, Hena Ghonia, N. 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