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Releases

All notable changes to this project will be documented in this file.

[0.4.2] - [Unreleased]

Added

  • Increased verbosity of function backtesting_forecaster().

Changed

  • Function backtesting_forecaster() do not modify the original forecaster.

  • Removed argument set_out_sample_residuals in function backtesting_forecaster().

  • Function model_selection.time_series_spliter renamed to model_selection.time_series_splitter

Fixed

  • Methods get_coef and get_feature_importance of ForecasterAutoregMultiOutput class return proper feature names.

[0.4.1] - [2021-12-13]

Added

Changed

Fixed

  • fit and predict transform pandas series and dataframes to numpy arrays if regressor is XGBoost.

[0.4.0] - [2021-12-10]

Version 0.4 has undergone a huge code refactoring. Main changes are related to input-output formats (only pandas series and dataframes are allowed although internally numpy arrays are used for performance) and model validation methods (unified into backtesting with and without refit).

Added

  • ForecasterBase as parent class

Changed

  • Argument y must be pandas Series. Numpy ndarrays are not allowed anymore.

  • Argument exog must be pandas Series or pandas DataFrame. Numpy ndarrays are not allowed anymore.

  • Output of predict is a pandas Series with index according to the steps predicted.

  • Scikitlearn pipelines are allowed as regressors.

  • backtesting_forecaster and backtesting_forecaster_intervals have been combined in a single function.

    • It is possible to backtest forecasters already trained.
    • ForecasterAutoregMultiOutput allows incomplete folds.
    • It is possible to update out_sample_residuals with backtesting residuals.

  • cv_forecaster has the option to update out_sample_residuals with backtesting residuals.

  • backtesting_sarimax_statsmodels and cv_sarimax_statsmodels have been combined in a single function.

  • gridsearch_forecaster use backtesting as validation strategy with the option of refit.

  • Extended information when printing Forecaster object.

  • All static methods for checking and preprocessing inputs moved to module utils.

  • Remove deprecated class ForecasterCustom.

[0.3.0] - [2021-09-01]

Added

  • New module model_selection_statsmodels to cross-validate, backtesting and grid search AutoReg and SARIMAX models from statsmodels library:
    • backtesting_autoreg_statsmodels
    • cv_autoreg_statsmodels
    • backtesting_sarimax_statsmodels
    • cv_sarimax_statsmodels
    • grid_search_sarimax_statsmodels

Changed

  • cv_forecaster returns cross-validation metrics and cross-validation predictions.
  • Added an extra column for each parameter in the dataframe returned by grid_search_forecaster.
  • statsmodels 0.12.2 added to requirements

Fixed

[0.2.0] - [2021-08-26]

Added

Changed

  • New implementation of ForecasterAutoregMultiOutput. The training process in the new version creates a different X_train for each step. See Direct multi-step forecasting for more details. Old versión can be acces with skforecast.deprecated.ForecasterAutoregMultiOutput.

Fixed

[0.1.9] - 2121-07-27

Added

  • Logging total number of models to fit in grid_search_forecaster.

  • Class ForecasterAutoregCustom.

  • Method create_train_X_y to facilitate access to the training data matrix created from y and exog.

Changed

  • New implementation of ForecasterAutoregMultiOutput. The training process in the new version creates a different X_train for each step. See Direct multi-step forecasting for more details. Old versión can be acces with skforecast.deprecated.ForecasterAutoregMultiOutput.

  • Class ForecasterCustom has been renamed to ForecasterAutoregCustom. However, ForecasterCustom will still remain to keep backward compatibility.

  • Argument metric in cv_forecaster, backtesting_forecaster, grid_search_forecaster and backtesting_forecaster_intervals changed from 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_mean_absolute_percentage_error' to 'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'.

  • Check if argument metric in cv_forecaster, backtesting_forecaster, grid_search_forecaster and backtesting_forecaster_intervals is one of 'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error'.

  • time_series_spliter doesn't include the remaining observations in the last complete fold but in a new one when allow_incomplete_fold=True. Take in consideration that incomplete folds with few observations could overestimate or underestimate the validation metric.

Fixed

  • Update lags of ForecasterAutoregMultiOutput after grid_search_forecaster.

[0.1.8.1] - 2021-05-17

Added

  • set_out_sample_residuals method to store or update out of sample residuals used by predict_interval.

Changed

  • backtesting_forecaster_intervals and backtesting_forecaster print number of steps per fold.

  • Only stored up to 1000 residuals.

  • Improved verbose in backtesting_forecaster_intervals.

Fixed

  • Warning of inclompleted folds when using backtesting_forecast with a ForecasterAutoregMultiOutput.

  • ForecasterAutoregMultiOutput.predict allow exog data longer than needed (steps).

  • backtesting_forecast prints correctly the number of folds when remainder observations are cero.

  • Removed named argument X in self.regressor.predict(X) to allow using XGBoost regressor.

  • Values stored in self.last_window when training ForecasterAutoregMultiOutput.

[0.1.8] - 2021-04-02

Added

  • Class ForecasterAutoregMultiOutput.py: forecaster with direct multi-step predictions.
  • Method ForecasterCustom.predict_interval and ForecasterAutoreg.predict_interval: estimate prediction interval using bootstrapping.
  • skforecast.model_selection.backtesting_forecaster_intervals perform backtesting and return prediction intervals.

Changed

Fixed

[0.1.7] - 2021-03-19

Added

  • Class ForecasterCustom: same functionalities as ForecasterAutoreg but allows custom definition of predictors.

Changed

  • grid_search forecaster adapted to work with objects ForecasterCustom in addition to ForecasterAutoreg.

Fixed

[0.1.6] - 2021-03-14

Added

  • Method get_feature_importances to skforecast.ForecasterAutoreg.
  • Added backtesting strategy in grid_search_forecaster.
  • Added backtesting_forecast to skforecast.model_selection.

Changed

  • Method create_lags return a matrix where the order of columns match the ascending order of lags. For example, column 0 contains the values of the minimum lag used as predictor.
  • Renamed argument X to last_window in method predict.
  • Renamed ts_cv_forecaster to cv_forecaster.

Fixed

[0.1.4] - 2021-02-15

Added

  • Method get_coef to skforecast.ForecasterAutoreg.

Changed

Fixed