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Changelog

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

Legend
Feature New feature
Enhancement Improvement in existing functionality
API Change Changes in the API
Fix Bug fix

0.18.0 Sep 22, 2025

The main changes in this release are:

  • Feature New parameter fold_stride in TimeSeriesFold. This parameter controls how the start of the test set advances between consecutive folds during the backtesting_forecaster, backtesting_forecaster_multiseries and backtesting_sarimax functions. By default, fold_stride is equal to steps, which means that the test sets do not overlap and there are no gaps between them. However, if fold_stride is set to a value less than steps, the test sets will overlap, resulting in multiple forecasts for the same observations. Conversely, if fold_stride is set to a value greater than steps, gaps will be left between consecutive test sets. (#764)

  • Feature Added module drift_detection with class RangeDriftDetector to detect out-of-range values in both target and exogenous variables during prediction. This lightweight detector checks whether new observations fall outside the ranges seen during training, making it suitable for real-time and production environments. It supports both global exogenous variables and series-specific exogenous variables in multiseries forecasting.

  • Feature New function backtesting_gif_creator in the plot module to create a gif that visualizes the backtesting process.

  • Feature New function show_datasets_info to display information about all available datasets.

  • Feature New attribute __skforecast_tags__ and public method get_tags() in all forecasters which provide metadata about the forecaster, such as its capabilities and limitations. This attribute can be useful for introspection and understanding the behavior of different forecasters.

  • API Change Backtesting functions output DataFrame now includes a fold column to identify the fold number of each prediction.

  • Fix Fixed a bug that caused the gap to not be applied correctly in the backtesting_forecaster_multiseries function. (#1028)

  • Fix Fixed a bug that prevented the CatBoostRegressor from working with the ForecasterRecursiveMultiSeries. (#1039)

Added

  • New parameter fold_stride in TimeSeriesFold. This parameter controls how the start of the test set advances between consecutive folds during the backtesting_forecaster, backtesting_forecaster_multiseries and backtesting_sarimax functions. By default, fold_stride is equal to steps, which means that the test sets do not overlap and there are no gaps between them. However, if fold_stride is set to a value less than steps, the test sets will overlap, resulting in multiple forecasts for the same observations. Conversely, if fold_stride is set to a value greater than steps, gaps will be left between consecutive test sets. (#764)

  • Added module drift_detection with class RangeDriftDetector to detect out-of-range values in both target and exogenous variables during prediction. This lightweight detector checks whether new observations fall outside the ranges seen during training, making it suitable for real-time and production environments. It supports both global exogenous variables and series-specific exogenous variables in multiseries forecasting.

  • New function backtesting_gif_creator in the plot module to create a gif that visualizes the backtesting process.

  • New function show_datasets_info to display information about all available datasets.

  • New attribute __skforecast_tags__ and public method get_tags() in all forecasters which provide metadata about the forecaster, such as its capabilities and limitations. This attribute can be useful for introspection and understanding the behavior of different forecasters.

Changed

  • Backtesting functions output DataFrame now includes a fold column to identify the fold number of each prediction.

Fixed

0.17.0 Aug 11, 2025

The main changes in this release are:

  • Feature ForecasterEquivalentDate can now predict intervals using the conformal prediction framework.

  • Feature Created module experimental, this module contains experimental features that are not yet fully tested or may change in future releases.

  • Enhancement The ForecasterRNN and the function create_and_compile_model have been refactored to allow for the inclusion of exogenous variables. The forecaster can also make interval predictions using the conformal prediction framework.

  • API Change Input data passed to all functions/classes must have either a pandas RangeIndex or DatetimeIndex. Previously, if the input did not meet this condition, a RangeIndex starting at 0 was automatically generated. This behavior has been removed to ensure consistent and explicit handling of input data.

  • API Change ForecasterRecursiveMultiSeries now accepts three input types for the series data: a wide-format DataFrame, where each column corresponds to a different time series; a long-format DataFrame with a MultiIndex, where the first level indicates the series name and the second level is the time index; or a dictionary with series names as keys and pandas Series as values.

  • API Change ForecasterRecursiveMultiSeries now accepts exog input as a wide-format DataFrame, where each column corresponds to a different exogenous variable; a long-format DataFrame with a MultiIndex, where the first level indicates the series name to which it belongs and the second level is the time index; or a dictionary with series names as keys and pandas Series or DataFrames as values.

  • API Change The functions series_long_to_dict and exog_long_to_dict have been renamed to reshape_series_long_to_dict and reshape_exog_long_to_dict in the preprocessing module.

  • Fix A bug that prevented the use of initial_train_size as a date with the OneStepAheadFold during the hyperparameter search has been fixed.

  • Fix A bug that caused the data types to be set incorrectly when creating the predicting matrix with the create_predict_X method or when return_predictors=True in the backtesting_forecaster and backtesting_forecaster_multiseries functions has been fixed. The dtypes of the predictors are now set to match those of the training data.

  • Fix A bug that prevented the use of a pd.RangeIndex with the OneStepAheadFold during the hyperparameter search has been fixed.

Added

  • Added attribute exog_dtypes_out_ in all forecasters to store the data types of the exogenous variables used in training after the transformation applied by transformer_exog. If transformer_exog is not used, it is equal to exog_dtypes_in_.

  • Added function reshape_series_wide_to_long in the preprocessing module. This function reshapes a wide-format DataFrame where each column corresponds to a series into a long-format DataFrame with with a MultiIndex. The first level of the index is the series name and the second level is the time index.

  • Added metric symmetric_mean_absolute_percentage_error in the metrics module. This metric calculates the symmetric mean absolute percentage error (SMAPE) between the true values and the predicted values.

  • ForecasterEquivalentDate can now predict intervals using the conformal prediction framework.

  • Created module experimental, this module contains experimental features that are not yet fully tested or may change in future releases.

  • Include function calculate_distance_from_holiday in the experimental module. It calculates the number of days to the next holiday and the number of days since the last holiday in a DataFrame with a date column.

  • The ForecasterRNN and the function create_and_compile_model now support the inclusion of exogenous variables.

  • Added method predict_interval to the ForecasterRNN using the conformal prediction framework.

Changed

  • Input data passed to all functions/classes must have either a pandas RangeIndex or DatetimeIndex. Previously, if the input did not meet this condition, a RangeIndex starting at 0 was automatically generated. This behavior has been removed to ensure consistent and explicit handling of input data.

  • ForecasterRecursiveMultiSeries now accepts three input types for the series data: a wide-format DataFrame, where each column corresponds to a different time series; a long-format DataFrame with a MultiIndex, where the first level indicates the series name and the second level is the time index; or a dictionary with series names as keys and pandas Series as values.

  • ForecasterRecursiveMultiSeries now accepts exog input as a wide-format DataFrame, where each column corresponds to a different exogenous variable; a long-format DataFrame with a MultiIndex, where the first level indicates the series name to which it belongs and the second level is the time index; or a dictionary with series names as keys and pandas Series or DataFrames as values.

  • When predicting, ForecasterRecursiveMultiSeries does not require the exog input to have the same type as the one used during training.

  • Function series_long_to_dict renamed to reshape_series_long_to_dict in the preprocessing module. This function reshapes a long-format DataFrame with time series data into a dictionary format where each entry corresponds to a series.

  • Function exog_long_to_dict renamed to reshape_exog_long_to_dict in the preprocessing module. This function reshapes a long-format DataFrame with exogenous variables into a dictionary format where each entry corresponds to the exogenous variables of a series.

  • The create_and_compile_model function has been refactored. All arguments related with layers and compilation are now passed as a dictionary using the following arguments: recurrent_layers_kwargs, dense_layers_kwargs, output_dense_layer_kwargs, and compile_kwargs.

  • The arguments lags and steps were removed from the ForecasterRNN initialization. These arguments are now inferred from the regressor architecture.

  • Remove preprocess_y, preprocess_last_window and preprocess_exog in favor of check_extract_values_and_index in the utils module. This function checks if the index is a pandas DatetimeIndex or RangeIndex and extracts the values and index accordingly.

Fixed

  • A bug that prevented the use of initial_train_size as a date with the OneStepAheadFold during the hyperparameter search has been fixed.

  • A bug that caused the data types to be set incorrectly when creating the predicting matrix with the create_predict_X method or when return_predictors=True in the backtesting_forecaster and backtesting_forecaster_multiseries functions has been fixed. The dtypes of the predictors are now set to match those of the training data.

  • A bug that prevented the use of a pd.RangeIndex with the OneStepAheadFold during the hyperparameter search has been fixed.

0.16.0 May 01, 2025

The main changes in this release are:

  • Enhancement Refactored the internal codebase of all forecasters to enhance performance, primarily by replacing pandas DataFrames with more efficient NumPy arrays.

Added

  • Function set_cpu_gpu_device() in the utils module to set the device of the regressor to 'cpu' or 'gpu'. It is used to ensure that the recursive prediction is done in cpu even if the regressor is set to 'gpu'. This allows to avoid the bottleneck of the recursive prediction when using a gpu. Only applied to recursive forecasters when the regressor is a XGBoost, LightGBM or CatBoost model.

  • Added series_name_in_ attribute in single series forecasters to store the name of the series used to fit the forecaster.

  • Added argument return_predictors to backtesting_forecaster and backtesting_forecaster_multiseries to return the predictors generated during the backtesting process along with the predictions.

Changed

  • Refactored the internal codebase of all forecasters to enhance performance, primarily by replacing pandas DataFrames with more efficient NumPy arrays.

  • In-sample residuals in direct forecasters has been simplified.

  • The method create_predict_X in the ForecasterRecursiveMultiSeries now returns a long-format DataFrame with the predictors. The columns are level and one column for each predictor. The index is the same as the prediction index.

  • The method create_predict_X in the ForecasterDirectMultiVariate now includes the level column in the returned DataFrame. The columns are level and one column for each predictor. The index is the same as the prediction index.

Fixed

0.15.1 Mar 18, 2025

  • Fix Minor release to fix a bug when importing module skforecast.sarimax.

Added

Changed

Fixed

  • Fixed import error when importing the skforecast.sarimax module.

0.15.0 Mar 10, 2025

The main changes in this release are:

Added

  • Support for Python 3.13.

  • Added rich>=13.9.4 library as hard dependence.

  • New argument method = 'conformal' in predict_interval method or interval_method = 'conformal' in backtesting functions to use the conformal prediction framework.

  • New class ConformalIntervalCalibrator to perform conformal calibration. This class is used to calibrate the prediction intervals using the conformal prediction framework.

  • Binned residuals are now available in the ForecasterRecursiveMultiSeries, ForecasterDirect and ForecasterDirectMultiVariate forecasters.

  • New method set_in_sample_residuals to store the in-sample residuals after fitting the forecaster using the same training data.

  • Functions crps_from_predictions and crps_from_quantiles in module metrics to calculate the Continuous Ranked Probability Score (CRPS).

  • Function calculate_coverage in module metrics to calculate the coverage of the predicted intervals.

  • The differentiation argument in ForecasterRecursiveMultiSeries can now be a dict to differentiate each series independently. This is useful if the user wants to differentiate each series with a different order or not differentiate some of them.

  • Added statistic ewm (exponential weighted mean) in RollingFeatures. Alpha can be specified using the new argument kwargs_stats, default {'ewm': {'alpha': 0.3}}.

  • Added method _repr_html_ to ForecasterSarimax, TimeSeriesFold and OneStepAheadFold to display the object in HTML format.

  • Added argument consolidate_dtypes in exog_long_to_dict function to ensure that the data types of the exogenous variables are consistent across all series when np.nan values are added and integer columns are converted to float.

  • Added calculate_lag_autocorrelation function to the plot module to calculate the autocorrelation and partial autocorrelation of a time series.

  • Added datasets m5, ett_m1, ett_m2, ett_m2_extended and expenditures_australia and public_transport_madrid to the datasets module.

  • Added function create_mean_pinball_loss in the metrics module to create a function to calculate the mean pinball loss for a given quantile.

  • Added function check_one_step_ahead_input to check the input data when using a OneStepAheadFold in the model_selection functions.

  • Function set_warnings_style in the exceptions module to set the style of the skforecast warnings issued by the library.

Changed

Fixed

  • Now ForecasterRecursiveMultiSeries can be saved correctly when weight_func is a dict with None for any series. It now use the method _weight_func_all_1 to create the weight function for these series.

  • Fix transform_numpy function in the utils module to work when transformers output in scikit-learn is set_output(transform='pandas').

0.14.0 Nov 11, 2024

The main changes in this release are:

This release has undergone a major refactoring to improve the performance of the library. Visit the migration guide section for more information.

  • Feature Window features can be added to the training matrix using the window_features argument in all forecasters. You can use the RollingFeatures class to create these features or create your own object. Create window and custom features.

  • Feature model_selection functions now have a new argument cv. This argument expect an object of type TimeSeriesFold (backtesting) or OneStepAheadFold which allows to define the validation strategy using the arguments initial_train_size, steps, gap, refit, fixed_train_size, skip_folds and allow_incomplete_folds.

  • Feature Hyperparameter search now allows to follow a one-step-ahead validation strategy using a OneStepAheadFold as cv argument in the model_selection functions.

  • Enhancement Refactor the prediction process in ForecasterRecursiveMultiSeries to improve performance when predicting multiple series.

  • Enhancement The bootstrapping process in the predict_bootstrapping method of all forecasters has been optimized to improve performance. This may result in slightly different results when using the same seed as in previous versions.

  • Enhancement Exogenous variables can be added to the training matrix if they do not contain the first window size observations. This is useful when exogenous variables are not available in early historical data. Visit the exogenous variables section for more information.

  • API Change Package structure has been changed to improve code organization. The forecasters have been grouped into the recursive, direct amd deep_learning modules. Visit the migration guide section for more information.

  • API Change ForecasterAutoregCustom has been deprecated. Window features can be added using the window_features argument in the ForecasterRecursive.

  • API Change Refactor the set_out_sample_residuals method in all forecasters, it now expects y_true and y_pred as arguments instead of residuals. This method is used to store the residuals of the out-of-sample predictions.

  • API Change The pmdarima.ARIMA regressor is no longer supported by the ForecasterSarimax. You can use the skforecast Sarimax model or, to continue using it, use skforecast 0.13.0 or lower.

  • Fix Fixed a bug where the create_predict_X method in recursive Forecasters did not correctly generate the matrix correctly when using transformations and/or differentiations

Added

  • Added numba>=0.59 as hard dependency.

  • Added window_features argument to all forecasters. This argument allows the user to add window features to the training matrix. See RollingFeatures.

  • Hyperparameter search now allows to follow a one-step-ahead validation strategy using a OneStepAheadFold as cv argument in the model_selection functions.

  • Differentiation has been extended to all forecasters. The differentiation argument has been added to all forecasters to model the n-order differentiated time series.

  • Create transform_numpy function in the utils module to carry out the transformation of the modeled time series and exogenous variables as numpy arrays.

  • random_state argument in the fit method of ForecasterRecursive to set a seed for the random generator so that the stored sample residuals are always deterministic.

  • New private method _train_test_split_one_step_ahead in all forecasters.

  • New private function _calculate_metrics_one_step_ahead to model_selection module to calculate the metrics when predicting one step ahead.

  • The steps argument in the predict method of the ForecasterRecursive can now be a str or a pandas datetime. If so, the method will predict up to the specified date. (contribution by @imMoya #811).

  • Exogenous variables can be added to the training matrix if they do not contain the first window size observations. This is useful when exogenous variables are not available in early historical data.

  • Added support for different activation functions in the create_and_compile_model function. (contribution by @pablorodriper #824).

Changed

  • ForecasterAutoregCustom and ForecasterAutoregMultiSeriesCustom has been deprecated. Window features can be added using the window_features argument in the ForecasterRecursive and ForecasterRecursiveMultiSeries.

  • Refactor recursive_predict in ForecasterRecursiveMultiSeries to predict all series at once and include option of adding residuals. This improves performance when predicting multiple series.

  • Refactor predict_bootstrapping in all Forecasters. The bootstrapping process has been optimized to improve performance. This may result in slightly different results when using the same seed as in previous versions.

  • Change the default value of encoding to ordinal in ForecasterRecursiveMultiSeries. This will avoid conflicts if the regressor does not support categorical variables by default.

  • Removed argument engine from bayesian_search_forecaster and bayesian_search_forecaster_multiseries.

  • The pmdarima.ARIMA regressor is no longer supported by the ForecasterSarimax. You can use the skforecast Sarimax model or, to continue using it, use skforecast 0.13.0 or lower.

  • initialize_lags in utils now returns the maximum lag, max_lag.

  • Removed attribute window_size_diff from all Forecasters. The window size extended by the order of differentiation is now calculated on window_size.

  • lags can be None when initializing any Forecaster that includes window features.

  • model_selection module has been divided internally into different modules to improve code organization (_validation, _search, _split).

  • Functions from model_selection_multiseries and model_selection_sarimax modules have been moved to the model_selection module.

  • model_selection functions now have a new argument cv. This argument expect an object of type TimeSeriesFold or OneStepAheadFold which allows to define the validation strategy using the arguments initial_train_size, steps, gap, refit, fixed_train_size, skip_folds and allow_incomplete_folds.

  • Added feature_selection module. The functions select_features and select_features_multiseries have been moved to this module.

  • The functions select_features and select_features_multiseries now have 3 returns: selected_lags, selected_window_features and selected_exog.

  • Refactor the set_out_sample_residuals method in all forecasters, it now expects y_true and y_pred as arguments instead of residuals.

  • exog_to_direct and exog_to_direct_numpy in utils now returns a the names of the columns of the transformed exogenous variables.

  • Renamed attributes in all Forecasters:

    • encoding_mapping has been renamed to encoding_mapping_.

    • last_window has been renamed to last_window_.

    • index_type has been renamed to index_type_.

    • index_freq has been renamed to index_freq_.

    • training_range has been renamed to training_range_.

    • series_col_names has been renamed to series_names_in_.

    • included_exog has been renamed to exog_in_.

    • exog_type has been renamed to exog_type_in_.

    • exog_dtypes has been renamed to exog_dtypes_in_.

    • exog_col_names has been renamed to exog_names_in_.

    • series_X_train has been renamed to X_train_series_names_in_.

    • X_train_col_names has been renamed to X_train_features_names_out_.

    • binner_intervals has been renamed to binner_intervals_.

    • in_sample_residuals has been renamed to in_sample_residuals_.

    • out_sample_residuals has been renamed to out_sample_residuals_.

    • fitted has been renamed to is_fitted.

  • Renamed arguments in different functions and methods:

    • in_sample_residuals has been renamed to use_in_sample_residuals.

    • binned_residuals has been renamed to use_binned_residuals.

    • series_col_names has been renamed to series_names_in_ in the check_predict_input, check_preprocess_exog_multiseries and initialize_transformer_series functions in the utils module.

    • series_X_train has been renamed to X_train_series_names_in_ in the prepare_levels_multiseries function in the utils module.

    • exog_col_names has been renamed to exog_names_in_ in the check_predict_input and check_preprocess_exog_multiseries functions in the utils module.

    • index_type has been renamed to index_type_ in the check_predict_input function in the utils module.

    • index_freq has been renamed to index_freq_ in the check_predict_input function in the utils module.

    • included_exog has been renamed to exog_in_ in the check_predict_input function in the utils module.

    • exog_type has been renamed to exog_type_in_ in the check_predict_input function in the utils module.

    • exog_dtypes has been renamed to exog_dtypes_in_ in the check_predict_input function in the utils module.

    • fitted has been renamed to is_fitted in the check_predict_input function in the utils module.

    • use_in_sample has been renamed to use_in_sample_residuals in the prepare_residuals_multiseries function in the utils module.

    • in_sample_residuals has been renamed to use_in_sample_residuals in the backtesting_forecaster, backtesting_forecaster_multiseries and check_backtesting_input (utils module) functions.

  • binned_residuals has been renamed to use_binned_residuals in the backtesting_forecaster function.

    • in_sample_residuals has been renamed to in_sample_residuals_ in the prepare_residuals_multiseries function in the utils module.

    • out_sample_residuals has been renamed to out_sample_residuals_ in the prepare_residuals_multiseries function in the utils module.

    • last_window has been renamed to last_window_ in the preprocess_levels_self_last_window_multiseries function in the utils module.

Fixed

  • Fixed a bug where the create_predict_X method in recursive Forecasters did not correctly generate the matrix correctly when using transformations and/or differentiations.

0.13.0 Aug 01, 2024

The main changes in this release are:

Added

  • Support for Python 3.12.

  • keras has been added as an optional dependency, tag deeplearning, to use the ForecasterRnn.

  • PyTorch backend for the ForecasterRnn.

  • New create_predict_X method in all recursive and direct Forecasters to allow the user to inspect the matrix passed to the predict method of the regressor.

  • New _create_predict_inputs method in all Forecasters to unify the inputs of the predict methods.

  • New plot function plot_prediction_intervals in the plot module to plot predicted intervals.

  • New module metrics with functions to calculate metrics for time series forecasting such as mean_absolute_scaled_error and root_mean_squared_scaled_error.

  • New argument skip_folds in model_selection and model_selection_multiseries functions. It allows the user to skip some folds during backtesting, which can be useful to speed up the backtesting process and thus the hyperparameter search.

  • New function plot_prediction_intervals in module plot.

  • Global Forecasters ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom are able to predict series not seen during training. This is useful when the user wants to predict a new series that was not included in the training data.

  • encoding can be set to None in Global Forecasters ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom. This option does not add the encoded series ids to the regressor training matrix.

  • New argument add_aggregated_metric in backtesting_forecaster_multiseries to include, in addition to the metrics for each level, the aggregated metric of all levels using the average (arithmetic mean), weighted average (weighted by the number of predicted values of each level) or pooling (the values of all levels are pooled and then the metric is calculated).

  • New argument aggregate_metric in grid_search_forecaster_multiseries, random_search_forecaster_multiseries and bayesian_search_forecaster_multiseries to select the aggregation method used to combine the metric(s) of all levels during the hyperparameter search. The available methods are: mean (arithmetic mean), weighted (weighted by the number of predicted values of each level) and pool (the values of all levels are pooled and then the metric is calculated). If more than one metric and/or aggregation method is used, all are reported in the results, but the first of each is used to select the best model.

  • New class DateTimeFeatureTransformer and function create_datetime_features in the preprocessing module to create datetime and calendar features from a datetime index.

Changed

Fixed

0.12.1 May 20, 2024

Fix This is a minor release to fix a bug.

Added

Changed

Fixed

  • Bug fix when storing last_window using a [ForecasterAutoregMultiSeries] that includes differentiation.

0.12.0 May 05, 2024

The main changes in this release are:

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 bootstrapped 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 Nov 16, 2023

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.skforecast_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 Sep 26, 2023

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 Sep 07, 2023

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 Jul 14, 2023

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 Jul 09, 2023

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 May 27, 2023

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 May 16, 2023

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 Mar 21, 2023

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 Nov 30, 2022

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 Oct 05, 2022

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 Sep 23, 2022

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 Mar 18, 2022

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 Jan 08, 2022

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 Dec 13, 2021

Added

Changed

Fixed

  • fit and predict transform pandas Series and DataFrames to numpy arrays if regressor is XGBoost.

0.4.0 Dec 10, 2021

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.

  • Scikit-learn 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 Sep 01, 2021

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 Aug 26, 2021

Added

  • Multiple exogenous variables can be passed as pandas DataFrame.

  • Documentation at https://skforecast.org

  • 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 version can be access with skforecast.deprecated.ForecasterAutoregMultiOutput.

Fixed

0.1.9 Jul 27, 2021

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 version can be accessed 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 May 17, 2021

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 incomplete 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 Apr 02, 2021

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 Mar 19, 2021

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 Mar 14, 2021

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 Feb 15, 2021

Added

  • Method get_coef to skforecast.ForecasterAutoreg.

Changed

Fixed