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Change Log

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

[0.12.0] - [2024-05-05]

The main changes in this release are:

  • Multiseries forecaster (Global Models) can be trained using series of different lengths and with different exogenous variables per series.

  • Bayesian hyperparameter search is now available for all multiseries forecasters using optuna as the search engine.

  • New functionality to select features using scikit-learn selectors (select_features and select_features_multiseries).

  • Added new forecaster ForecasterRnn to create forecasting models based on deep learning (RNN and LSTM).

  • New method to predict intervals conditioned on the range of the predicted values. This is can help to improve the interval coverage when the residuals are not homoscedastic (ForecasterAutoreg).

  • All Recursive Forecasters are now able to differentiate the time series before modeling it.

Added

  • Added bayesian_search_forecaster_multiseries function to model_selection_multiseries module. This function performs a Bayesian hyperparameter search for the ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom, and ForecasterAutoregMultiVariate using optuna as the search engine.

  • ForecasterAutoregMultiVariate allows to include None when lags is a dict so that a series does not participate in the construction of X_train.

  • The output_file argument has been added to the hyperparameter search functions in the model_selection, model_selection_multiseries and model_selection_sarimax modules to save the results of the hyperparameter search in a tab-separated values (TSV) file.

  • New argument binned_residuals in method predict_interval allows to condition the bootstraped residuals on range of the predicted values.

  • Added save_custom_functions argument to the save_forecaster function in the utils module. If True, save custom functions used in the forecaster (fun_predictors and weight_func) as .py files. Custom functions must be available in the environment where the forecaster is loaded.

  • Added select_features and select_features_multiseries functions to the model_selection and model_selection_multiseries modules to perform feature selection using scikit-learn selectors.

  • Added sort_importance argument to get_feature_importances method in all Forecasters. If True, sort the feature importances in descending order.

  • Added initialize_lags_grid function to model_selection module. This function initializes the lags to be used in the hyperparameter search functions in model_selection and model_selection_multiseries.

  • Added _initialize_levels_model_selection_multiseries function to model_selection_multiseries module. This function initializes the levels of the series to be used in the model selection functions.

  • Added set_dark_theme function to the plot module to set a dark theme for matplotlib plots.

  • Allow tuple type for lags argument in all Forecasters.

  • Argument differentiation in all Forecasters to model the n-order differentiated time series.

  • Added window_size_diff attribute to all Forecasters. It stores the size of the window (window_size) extended by the order of differentiation. Added to all Forecasters for API consistency.

  • Added store_last_window parameter to fit method in Forecasters. If True, store the last window of the training data.

  • Added utils.set_skforecast_warnings function to set the warnings of the skforecast package.

  • Added new forecaster ForecasterRnn to create forecasting models based on deep learning (RNN and LSTM).

  • Added new function create_and_compile_model to module skforecast.ForecasterRnn.utils to help to create and compile a RNN or LSTM models to be used in ForecasterRnn.

Changed

  • Deprecated argument lags_grid in bayesian_search_forecaster. Use search_space to define the candidate values for the lags. This allows the lags to be optimized along with the other hyperparameters of the regressor in the bayesian search.

  • n_boot argument in predict_intervalchanged from 500 to 250.

  • Changed the default value of the transformer_series argument to use a StandardScaler() in the Global Forecasters (ForecasterAutoregMultiSeries, ForecasterAutoregMultiSeriesCustom and ForecasterAutoregMultiVariate).

  • Refactor utils.select_n_jobs_backtesting to use the forecaster directly instead of forecaster_name and regressor_name.

  • Remove _backtesting_forecaster_verbose in model_selection in favor of _create_backtesting_folds, (deprecated since 0.8.0).

Fixed

  • Small bug in utils.select_n_jobs_backtesting, rename ForecasterAutoregMultiseries to ForecasterAutoregMultiSeries.

[0.11.0] - [2023-11-16]

The main changes in this release are:

  • New predict_quantiles method in all Autoreg Forecasters to calculate the specified quantiles for each step.

  • Create ForecasterBaseline.ForecasterEquivalentDate, a Forecaster to create simple model that serves as a basic reference for evaluating the performance of more complex models.

Added

  • Added skforecast.datasets module. It contains functions to load data for our examples and user guides.

  • Added predict_quantiles method to all Autoreg Forecasters.

  • Added SkforecastVersionWarning to the exception module. This warning notify that the skforecast version installed in the environment differs from the version used to initialize the forecaster when using load_forecaster.

  • Create ForecasterBaseline.ForecasterEquivalentDate, a Forecaster to create simple model that serves as a basic reference for evaluating the performance of more complex models.

Changed

  • Enhance the management of internal copying in skforecast to minimize the number of copies, thereby accelerating data processing.

Fixed

  • Rename self.skforcast_version attribute to self.skforecast_version in all Forecasters.

  • Fixed a bug where the create_train_X_y method did not correctly align lags and exogenous variables when the index was not a Pandas index in all Forecasters.

[0.10.1] - [2023-09-26]

This is a minor release to fix a bug when using grid_search_forecaster, random_search_forecaster or bayesian_search_forecaster with a Forecaster that includes differentiation.

Added

Changed

Fixed

  • Bug fix grid_search_forecaster, random_search_forecaster or bayesian_search_forecaster with a Forecaster that includes differentiation.

[0.10.0] - [2023-09-07]

The main changes in this release are:

  • New Sarimax.Sarimax model. A wrapper of statsmodels.SARIMAX that follows the scikit-learn API and can be used with the ForecasterSarimax.

  • Added differentiation argument to ForecasterAutoreg and ForecasterAutoregCustom to model the n-order differentiated time series using the new skforecast preprocessor TimeSeriesDifferentiator.

Added

  • New Sarimax.Sarimax model. A wrapper of statsmodels.SARIMAX that follows the scikit-learn API.

  • Added skforecast.preprocessing.TimeSeriesDifferentiator to preprocess time series by differentiating or integrating them (reverse differentiation).

  • Added differentiation argument to ForecasterAutoreg and ForecasterAutoregCustom to model the n-order differentiated time series.

Changed

  • Refactor ForecasterSarimax to work with both skforecast Sarimax and pmdarima ARIMA models.

  • Replace setup.py with pyproject.toml.

Fixed

[0.9.1] - [2023-07-14]

The main changes in this release are:

  • Fix imports in skforecast.utils module to correctly import sklearn.linear_model into the select_n_jobs_backtesting and select_n_jobs_fit_forecaster functions.

Added

Changed

Fixed

  • Fix imports in skforecast.utils module to correctly import sklearn.linear_model into the select_n_jobs_backtesting and select_n_jobs_fit_forecaster functions.

[0.9.0] - [2023-07-09]

The main changes in this release are:

  • ForecasterAutoregDirect and ForecasterAutoregMultiVariate include the n_jobs argument in their fit method, allowing multi-process parallelization for improved performance.

  • All backtesting and grid search functions have been extended to include the n_jobs argument, allowing multi-process parallelization for improved performance.

  • Argument refit now can be also an integer in all backtesting dependent functions in modules model_selection, model_selection_multiseries, and model_selection_sarimax. This allows the Forecaster to be trained every this number of iterations.

  • ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom can be trained using series of different lengths. This means that the model can handle datasets with different numbers of data points in each series.

Added

  • Support for scikit-learn 1.3.x.

  • Argument n_jobs='auto' to fit method in ForecasterAutoregDirect and ForecasterAutoregMultiVariate to allow multi-process parallelization.

  • Argument n_jobs='auto' to all backtesting dependent functions in modules model_selection, model_selection_multiseries and model_selection_sarimax to allow multi-process parallelization.

  • Argument refit now can be also an integer in all backtesting dependent functions in modules model_selection, model_selection_multiseries, and model_selection_sarimax. This allows the Forecaster to be trained every this number of iterations.

  • ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom allow to use series of different lengths for training.

  • Added show_progress to grid search functions.

  • Added functions select_n_jobs_backtesting and select_n_jobs_fit_forecaster to utils to select the number of jobs to use during multi-process parallelization.

Changed

  • Remove get_feature_importance in favor of get_feature_importances in all Forecasters, (deprecated since 0.8.0).

  • The model_selection._create_backtesting_folds function now also returns the last window indices and whether or not to train the forecaster.

  • The model_selection functions _backtesting_forecaster_refit and _backtesting_forecaster_no_refit have been unified in _backtesting_forecaster.

  • The model_selection_multiseries functions _backtesting_forecaster_multiseries_refit and _backtesting_forecaster_multiseries_no_refit have been unified in _backtesting_forecaster_multiseries.

  • The model_selection_sarimax functions _backtesting_refit_sarimax and _backtesting_no_refit_sarimax have been unified in _backtesting_sarimax.

  • utils.preprocess_y allows a pandas DataFrame as input.

Fixed

  • Ensure reproducibility of Direct Forecasters when using predict_bootstrapping, predict_dist and predict_interval with a list of steps.

  • The create_train_X_y method returns a dict of pandas Series as y_train in ForecasterAutoregDirect and ForecasterAutoregMultiVariate. This ensures that each series has the appropriate index according to the step to be trained.

  • The filter_train_X_y_for_step method in ForecasterAutoregDirect and ForecasterAutoregMultiVariate now updates the index of X_train_step to ensure correct alignment with y_train_step.

[0.8.1] - [2023-05-27]

Added

  • Argument store_in_sample_residuals=True in fit method added to all forecasters to speed up functions such as backtesting.

Changed

  • Refactor utils.exog_to_direct and utils.exog_to_direct_numpy to increase performance.

Fixed

[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 function model_selection._create_backtesting_folds to provide train/test indices (position) for backtesting functions.

  • 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

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