CRAN v0.1.0

• First release.

New features

Data structures

• Added the mable (model table) data class (mdl_df) which is a tibble-like data structure for applying multiple models to a dataset. Each row of the mable refers to a different time series from the data (identified by the key columns). A mable must contain at least one column of time series models (mdl_ts), where the list column itself (lst_mdl) describes how these models are related.
• Added the fable (forecast table) data class (fbl_ts) which is a tsibble-like data structure for representing forecasts. In extension to the key and index from the tsibble (tbl_ts) class, a fable (fbl_ts) must contain columns of point forecasts for the response variable(s), and a single distribution column (fcdist).
• Added the dable (decomposition table) data class (dcmp_ts) which is a tsibble-like data structure for representing decompositions. This data class is useful for representing decompositions, as its print method describes how its columns can be combined to produce the original data, and has a more appropriate autoplot() method for displaying decompositions. Beyond this, a dable (dcmp_ts) behaves very similarly to a tsibble (tbl_ts).

Modelling

• Support for model (new_model_class(), new_model_definition()) and decomposition definitions (new_decomposition_class(), new_decomposition_definition()).
• Added parsing tools to compactly specify models using a formula interface. Transformations specified on left hand side, where the response variable is determined by object length. In case of a conflict in object length, such as GDP/CPI, the response will be the ratio of the pair. To transform a variable by some other data variable, the response can be specified using resp(), giving resp(GDP)/CPI. Multiple variables (and separate transformations for each), can be specified using vars(): vars(log(GDP), CPI). The inputs to the model are specified on the right hand side, and are handled using model defined specials (new_specials()).
• Added methods to train a model definition to a dataset. model() is the recommended interface, which can fit many model definitions to each time series in the input dataset returning a mable (mdl_df). The lower level interface for model estimation is accessible using estimate() which will return a time series model (mdl_ts), however using this interface is discouraged.

Forecasting

• Added forecast(), which allows you to produce future predictions of a time series from fitted models. The methods provided in fabletools handle the application of new data (such as the future index or exogenous regressors) to model specials, giving a simple and consistent interface to forecasting any model. The forecast methods will automatically backtransform and bias adjust any transformations specified in the model formula. This function returns a fable (fbl_ts) object.
• Added a forecast distribution class (fcdist) which is used to describe the distribution of forecasts. Common forecast distributions have been added to the package, including the normal distribution (dist_normal()), multivariate normal (dist_mv_normal()) and simulated/sampled distributions (dist_sim()). In addition to this, dist_unknown() is available for methods that don't support distributional forecasts. A new distribution can be added using the new_fcdist() function. The forecast distribution class handles transformations on the distribution, and is used to create forecast intervals of the hilo class using the hilo() function. Mathematical operations on the normal distribution are supported.
• Added tools for working with transformations in models, including automatic back-transformation, transformation classes (new_transformation()), and bias adjustment (bias_adjust()) methods.
• Added aggregate_key(), which is used to compute all levels of aggregation in a specified key structure. It supports nested structures using parent / key and crossed structures using keyA * keyB.
• Added support for forecast reconciliation using reconcile(). This function modifies the way in which forecasts from a model column are combined to give coherent forecasts. In this version the MinT (min_trace()) reconciliation technique is available. This is commonly used in combination with aggregate_key().

Generics

• Added broom package functionality for augment(), tidy(), and glance().
• Added components(), which returns a dable (dcmp_ts) that describes how the fitted values of a model were obtained from its components. This is commonly used to visualise the states of a state space model.
• Added equation(), which returns a formatted display of a fitted model's equation. This is commonly used to conveniently add model equations to reports, and to better understand the structure of the model.
• Added accessors to common model data elements: fitted values with fitted(), model residuals with residuals(), and the response variable with response(). These functions return a tsibble (tbl_ts) object.
• Added refit(), which allows an estimated model to be applied to a new dataset.
• Added report(), which provides a detailed summary of an estimated model.
• Added generate() support, which is used to simulate future paths from an estimated model.
• Added stream(), which allows an estimated model to be extended using newly available data.
• Added interpolate(), which allows missing values from a dataset to be interpolated using an estimated model (and model appropriate interpolation strategy).
• Added features(), along with scoped variants features_at(), features_if() and features_all(). These functions make it easy to compute a large collection of features for each time series in the input dataset.
• Added feature_set(), which allows a collection of registered features from loaded packages to be accessed using a tagging system.

Models

• Added decomposition_model(), which allows the components from any decomposition method that returns a dable (dcmp_ts) to be modelled separately and have their forecasts combined to give forecasts on the original response variable.
• Added combination_model(), which allows any model to be combined with any other. This function accepts a function which describes how the models are combined (such as combination_ensemble()). A combination model can also be obtained by using mathematical operations on model definitions or estimated models.
• Added null_model(), which can be used as a empty model in a mable (mdl_df). This is most commonly used as a substitute for models which encountered an error, preventing the successfully estimated models from being lost.

Evaluation

• Added accuracy(), which allows the accuracy of a model to be evaluated. This function can be used to summarise model performance on the training data (accuracy.mdl_df(), accuracy.mdl_ts()), or to evaluate the accuracy of forecasts over a test dataset (accuracy.fbl_ts()). Several accuracy measures are supported, including point_accuracy_measures (ME, MSE, RMSE, MAE, MPE, MAPE, MASE, ACF1), interval_accuracy_measures (winkler_score) and distribution_accuracy_measures (percentile_score). These accuracy functions can be used in conjunction with the rolling functions in the tsibble package (stretch_tsibble(), slide_tsibble(), tile_tsibble()) to computed time series cross-validated accuracy measures.