finnts v0.5.0

What's Changed

• Fixed bug of having run_type duplicated when joining dataframes by @mitokic in #151
• Update pkgdown.yaml by @mitokic in #152
• Mitokic/01272024/hts drivers by @mitokic in #153
• fix hts future drivers issue and feature selection issue by @mitokic in #156
• Mitokic/032024/multihorizon fcst models by @mitokic in #158
• fix feature engineering lags with multitep horizon by @mitokic in #159
• global model bug fix for multistep horizon forecasting by @mitokic in #161
• Mitokic/07172024/best model scaling by @mitokic in #163
• Mitokic/07292024/multistep bug by @mitokic in #164
• Mitokic/10102024/synapse 34 migration by @mitokic in #167
• Mitokic/10162024/hts drivers fix by @mitokic in #168
• Mitokic/10252024/cran submission by @mitokic in #169

Full Changelog: v0.4.0...v0.5.0

finnts v0.4.0

What's Changed

• tidymodels process update by @mitokic in #140
• Mitokic/08112023/feature selection by @mitokic in #141
• ARIMAX Implementation by @taixhi in #142
• Mitokic/08252023/hts recon error by @mitokic in #143
• Mitokic/08262023/diff boxcox by @mitokic in #144
• global models bug fix by @mitokic in #145
• fix box-cox transformation by @mitokic in #146
• add logic to prevent overly large forecast in hts recon process by @mitokic in #147
• fix residual issue in hts by @mitokic in #148
• Mitokic/11292023/cran submit by @mitokic in #149

New Contributors

• @taixhi made their first contribution in #142

Full Changelog: v0.3.0...v0.4.0

v0.3.0

finnts 0.3.0

Improvements

• Spark data frame support. Initial input data can now be a spark data frame, enabling millions of time series to be ran across a spark compute cluster.
• Updated train/validation/test process for multivariate ML models.
• In addition to existing forecast_time_series(), added new sub components of the finnts forecast process that can be called separately or in a production pipeline. Allows for more control of the forecast process
  • prep_data()
  • prep_models()
  • train_models()
  • ensemble_models()
  • final_models()
• Automated read and write capabilities. Intermediate and final Finn outputs are now automatically written to disk (see options below). This creates better MLOps capabilities, easier scale on spark, and better fault tolerance by not needing to start the whole forecast process over from scratch if an error occurred.
  • Temporary location on local machine, which will then get deleted after R session is closed.
  • Path on local machine or a mounted Azure Data Lake Storage path in spark to save the intermediate and final Finn run results.
  • Azure Blob Storage to store non-spark runs on a data lake. SharePoint/OneDrive storage to store non-spark runs within M365.
• New MLOps features that allow you to retrieve the final trained models through get_trained_models(), get specific run information thorough get_run_info(), and even retrieve the initial feature engineered data through get_prepped_data().

Deprecated

• run_model_parallel has been replaced with inner_parallel within forecast_time_series()
• Data being returned as a list when running forecast_time_series(). Instead please use get_forecast_data() to retrieve Finn forecast outputs.

Breaking Changes

• No longer support for Azure Batch parallel processing, please use spark instead
• Parallel processing through spark now needs a mounted Azure Data Lake Storage path supplied through set_run_info(). Please refer to the vignettes for more details.

v0.2.4

What's Changed

• Mitokic/timetk archive fix by @mitokic in #123

Full Changelog: v0.2.3...v0.2.4

v0.2.3

Merge pull request #122 from microsoft/mitokic/remove-modeltime.gluonts

removed modeltime.gluonts dependency

v0.2.4.9000

code review changes

finnts v0.2.2

What's Changed

• Typo in modelling approach by @DSLmsft in #112
• Compatibility with dplyr 1.1.0 by @lionel- in #117
• Users/mitokic/hts recon bug fix by @mitokic in #119
• Users/mitokic/cran 0.2.2 by @mitokic in #120

New Contributors

• @lionel- made their first contribution in #117

Full Changelog: v0.2.1...v0.2.2

v0.2.1

finnts 0.2.1

Bug Fixes

• Fixed feature engineering issue around NaN/Inf values when computing log values of negative external regressor values.
• Fixed issue of ensuring random seed is set correctly in parallel processing.

v0.2.0

finnts 0.2.0

Improvements

• Added spark support to run Finn in parallel on Azure Databricks or Azure Synapse.
• Added error handling when creating simple model averages. Should allow forecast to keep running even if there are memory issues when averaging individual forecast models, which helps on large data sets.
• Expand Azure Batch task timeout from one day to one week. Prevents errors when running large forecasts that take over a day to run in Azure Batch.

Deprecated

• Deprecated azure_batch parallel compute option within forecast_time_series function since the Azure Batch R packages are deprecated. Please use the new integration with spark on Azure.

v0.1.1

What's Changed

• Users/mitokic/ram bug fix by @mitokic in #96
• Users/mitokic/cran 0.1.1 by @mitokic in #97

Full Changelog: v0.1.0...v0.1.1