Change Log

All significant changes to this project are documented in this release file.

[0.8.0] - [2023-05-16]

Added

• Added the fit_kwargs argument to all forecasters to allow the inclusion of additional keyword arguments passed to the regressor's fit method.

• Added the set_fit_kwargs method to set the fit_kwargs attribute.

• Support for pandas 2.0.x.

• Added exceptions module with custom warnings.

• Added function utils.check_exog_dtypes to issue a warning if exogenous variables are one of type init, float, or category. Raise Exception if exog has categorical columns with non integer values.

• Added function utils.get_exog_dtypes to get the data types of the exogenous variables included during the training of the forecaster model.

• Added function utils.cast_exog_dtypes to cast data types of the exogenous variables using a dictionary as a mapping.

• Added function utils.check_select_fit_kwargs to check if the argument fit_kwargs is a dictionary and select only the keys used by the fit method of the regressor.

• Added argument gap to functions in model_selection, model_selection_multiseries and model_selection_sarimax to omit observations between training and prediction.

• Added argument show_progress to functions model_selection.backtesting_forecaster, model_selection_multiseries.backtesting_forecaster_multiseries and model_selection_sarimax.backtesting_forecaster_sarimax to indicate weather to show a progress bar.

• Added argument remove_suffix, default False, to the method filter_train_X_y_for_step() in ForecasterAutoregDirect and ForecasterAutoregMultiVariate. If remove_suffix=True the suffix "_step_i" will be removed from the column names of the training matrices.

Changed

• Rename optional dependency package statsmodels to sarimax. Now only pmdarima will be installed, statsmodels is no longer needed.

• Rename get_feature_importance() to get_feature_importances() in all Forecasters. get_feature_importance() method will me removed in skforecast 0.9.0.

• Refactor get_feature_importances() in all Forecasters.

• Remove model_selection_statsmodels in favor of ForecasterSarimax and model_selection_sarimax, (deprecated since 0.7.0).

• Remove attributes create_predictors and source_code_create_predictors in favor of fun_predictors and source_code_fun_predictors in ForecasterAutoregCustom, (deprecated since 0.7.0).

• The utils.check_exog function now includes a new optional parameter, allow_nan, that controls whether a warning should be issued if the input exog contains NaN values.

• utils.check_exog is applied before and after exog transformations.

• The utils.preprocess_y function now includes a new optional parameter, return_values, that controls whether to return a numpy ndarray with the values of y or not. This new option is intended to avoid copying data when it is not necessary.

• The utils.preprocess_exog function now includes a new optional parameter, return_values, that controls whether to return a numpy ndarray with the values of y or not. This new option is intended to avoid copying data when it is not necessary.

• Replaced tqdm.tqdm by tqdm.auto.tqdm.

• Refactor utils.exog_to_direct.

Fixed

• The dtypes of exogenous variables are maintained when generating the training matrices with the create_train_X_y method in all the Forecasters.

[0.7.0] - [2023-03-21]

Added

• Class ForecasterAutoregMultiSeriesCustom.

• Class ForecasterSarimax and model_selection_sarimax (wrapper of pmdarima).

• Method predict_interval() to ForecasterAutoregDirect and ForecasterAutoregMultiVariate.

• Method predict_bootstrapping() to all forecasters, generate multiple forecasting predictions using a bootstrapping process.

• Method predict_dist() to all forecasters, fit a given probability distribution for each step using a bootstrapping process.

• Function plot_prediction_distribution in module plot.

• Alias backtesting_forecaster_multivariate for backtesting_forecaster_multiseries in model_selection_multiseries module.

• Alias grid_search_forecaster_multivariate for grid_search_forecaster_multiseries in model_selection_multiseries module.

• Alias random_search_forecaster_multivariate for random_search_forecaster_multiseries in model_selection_multiseries module.

• Attribute forecaster_id to all Forecasters.

Changed

• Deprecated python 3.7 compatibility.

• Added python 3.11 compatibility.

• model_selection_statsmodels is deprecated in favor of ForecasterSarimax and model_selection_sarimax. It will be removed in version 0.8.0.

• Remove levels_weights argument in grid_search_forecaster_multiseries and random_search_forecaster_multiseries, deprecated since version 0.6.0. Use series_weights and weights_func when creating the forecaster instead.

• Attributes create_predictors and source_code_create_predictors renamed to fun_predictors and source_code_fun_predictors in ForecasterAutoregCustom. Old names will be removed in version 0.8.0.

• Remove engine 'skopt' in bayesian_search_forecaster in favor of engine 'optuna'. To continue using it, use skforecast 0.6.0.

• in_sample_residuals and out_sample_residuals are stored as numpy ndarrays instead of pandas series.

• In ForecasterAutoregMultiSeries, set_out_sample_residuals() is now expecting a dict for the residuals argument instead of a pandas DataFrame.

• Remove the scikit-optimize dependency.

Fixed

• Remove operator ** in set_params() method for all forecasters.

• Replace getfullargspec in favor of inspect.signature (contribution by @jordisilv).

[0.6.0] - [2022-11-30]

Added

• Class ForecasterAutoregMultivariate.

• Function initialize_lags in utils module to create lags values in the initialization of forecasters (applies to all forecasters).

• Function initialize_weights in utils module to check and initialize arguments series_weightsand weight_func (applies to all forecasters).

• Argument weights_func in all Forecasters to allow weighted time series forecasting. Individual time based weights can be assigned to each value of the series during the model training.

• Argument series_weights in ForecasterAutoregMultiSeries to define individual weights each series.

• Include argument random_state in all Forecasters set_out_sample_residuals methods for random sampling with reproducible output.

• In ForecasterAutoregMultiSeries, predict and predict_interval methods allow the simultaneous prediction of multiple levels.

• backtesting_forecaster_multiseries allows backtesting multiple levels simultaneously.

• metric argument can be a list in grid_search_forecaster_multiseries, random_search_forecaster_multiseries. If metric is a list, multiple metrics will be calculated. (suggested by Pablo Dávila Herrero https://github.com/Pablo-Davila)

• Function multivariate_time_series_corr in module utils.

• Function plot_multivariate_time_series_corr in module plot.

Changed

• ForecasterAutoregDirect allows to predict specific steps.

• Remove ForecasterAutoregMultiOutput in favor of ForecasterAutoregDirect, (deprecated since 0.5.0).

• Rename function exog_to_multi_output to exog_to_direct in utils module.

• In ForecasterAutoregMultiSeries, rename parameter series_levels to series_col_names.

• In ForecasterAutoregMultiSeries change type of out_sample_residuals to a dict of numpy ndarrays.

• In ForecasterAutoregMultiSeries, delete argument level from method set_out_sample_residuals.

• In ForecasterAutoregMultiSeries, level argument of predict and predict_interval renamed to levels.

• In backtesting_forecaster_multiseries, level argument of predict and predict_interval renamed to levels.

• In check_predict_input function, argument level renamed to levels and series_levels renamed to series_col_names.

• In backtesting_forecaster_multiseries, metrics_levels output is now a pandas DataFrame.

• In grid_search_forecaster_multiseries and random_search_forecaster_multiseries, argument levels_weights is deprecated since version 0.6.0, and will be removed in version 0.7.0. Use series_weights and weights_func when creating the forecaster instead.

• Refactor _create_lags_ in ForecasterAutoreg, ForecasterAutoregDirect and ForecasterAutoregMultiSeries. (suggested by Bennett https://github.com/Bennett561)

• Refactor backtesting_forecaster and backtesting_forecaster_multiseries.

• In ForecasterAutoregDirect, filter_train_X_y_for_step now starts at 1 (before 0).

• In ForecasterAutoregDirect, DataFrame y_train now start with 1, y_step_1 (before y_step_0).

• Remove cv_forecaster from module model_selection.

Fixed

• In ForecasterAutoregMultiSeries, argument last_window predict method now works when it is a pandas DataFrame.

• In ForecasterAutoregMultiSeries, fix bug transformers initialization.

[0.5.1] - [2022-10-05]

Added

• Check that exog and y have the same length in _evaluate_grid_hyperparameters and bayesian_search_forecaster to avoid fit exception when return_best.

• Check that exog and series have the same length in _evaluate_grid_hyperparameters_multiseries to avoid fit exception when return_best.

Changed

• Argument levels_list in grid_search_forecaster_multiseries, random_search_forecaster_multiseries and _evaluate_grid_hyperparameters_multiseries renamed to levels.

Fixed

• ForecasterAutoregMultiOutput updated to match ForecasterAutoregDirect.

• Fix Exception to raise when level_weights does not add up to a number close to 1.0 (before was exactly 1.0) in grid_search_forecaster_multiseries, random_search_forecaster_multiseries and _evaluate_grid_hyperparameters_multiseries.

• Create_train_X_y in ForecasterAutoregMultiSeries now works when the forecaster is not fitted.

[0.5.0] - [2022-09-23]

Added

• New arguments transformer_y (transformer_series for multiseries) and transformer_exog in all forecaster classes. It is for transforming (scaling, max-min, ...) the modeled time series and exogenous variables inside the forecaster.

• Functions in utils transform_series and transform_dataframe to carry out the transformation of the modeled time series and exogenous variables.

• Functions _backtesting_forecaster_verbose, random_search_forecaster, _evaluate_grid_hyperparameters, bayesian_search_forecaster, _bayesian_search_optuna and _bayesian_search_skopt in model_selection.

• Created ForecasterAutoregMultiSeries class for modeling multiple time series simultaneously.

• Created module model_selection_multiseries. Functions: _backtesting_forecaster_multiseries_refit, _backtesting_forecaster_multiseries_no_refit, backtesting_forecaster_multiseries, grid_search_forecaster_multiseries, random_search_forecaster_multiseries and _evaluate_grid_hyperparameters_multiseries.

• Function _check_interval in utils. (suggested by Thomas Karaouzene https://github.com/tkaraouzene)

• metric can be a list in backtesting_forecaster, grid_search_forecaster, random_search_forecaster, backtesting_forecaster_multiseries. If metric is a list, multiple metrics will be calculated. (suggested by Pablo Dávila Herrero https://github.com/Pablo-Davila)

• Skforecast works with python 3.10.

• Functions save_forecaster and load_forecaster to module utils.

• get_feature_importance() method checks if the forecast is fitted.

Changed

• backtesting_forecaster change default value of argument fixed_train_size: bool=True.

• Remove argument set_out_sample_residuals in function backtesting_forecaster (deprecated since 0.4.2).

• backtesting_forecaster verbose now includes fold size.

• grid_search_forecaster results include the name of the used metric as column name.

• Remove get_coef method from ForecasterAutoreg, ForecasterAutoregCustom and ForecasterAutoregMultiOutput (deprecated since 0.4.3).

• _get_metric now allows mean_squared_log_error.

• ForecasterAutoregMultiOutput has been renamed to ForecasterAutoregDirect. ForecasterAutoregMultiOutput will be removed in version 0.6.0.

• check_predict_input updated to check ForecasterAutoregMultiSeries inputs.

• set_out_sample_residuals has a new argument transform to transform the residuals before being stored.

Fixed

• fit now stores last_window values with len = forecaster.max_lag in ForecasterAutoreg and ForecasterAutoregCustom.

• in_sample_residuals stored as a pd.Series when len(residuals) > 1000.

[0.4.3] - [2022-03-18]

Added

• Checks if all elements in lags are int when creating ForecasterAutoreg and ForecasterAutoregMultiOutput.

• Add fixed_train_size: bool=False argument to backtesting_forecaster and backtesting_sarimax

Changed

• Rename get_metric to _get_metric.

• Functions in model_selection module allow custom metrics.

• Functions in model_selection_statsmodels module allow custom metrics.

• Change function set_out_sample_residuals (ForecasterAutoreg and ForecasterAutoregCustom), residuals argument must be a pandas Series (was numpy ndarray).

• Returned value of backtesting functions (model_selection and model_selection_statsmodels) is now a float (was numpy ndarray).

• get_coef and get_feature_importance methods unified in get_feature_importance.

Fixed

• Requirements versions.

• Method fit doesn't remove out_sample_residuals each time the forecaster is fitted.

• Added random seed to residuals downsampling (ForecasterAutoreg and ForecasterAutoregCustom)

[0.4.2] - [2022-01-08]

Added

• Increased verbosity of function backtesting_forecaster().

• Random state argument in backtesting_forecaster().

Changed

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

• Deprecated 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.

Fixed

[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

• Added attribute window_size to ForecasterAutoreg and ForecasterAutoregCustom. It is equal to max_lag.

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

• Multiple exogenous variables can be passed as pandas DataFrame.

• Documentation at https://joaquinamatrodrigo.github.io/skforecast/

• New unit test

• Increased typing

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] - [2021-07-27]

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