// Complete constructor signatures and method signatures for all skforecast forecasters, backtesting functions, search functions, cross-validation classes, preprocessing, feature selection, and drift detection. Use when the user needs exact parameter names, types, or defaults for any skforecast class or function.
| name | complete-api-reference |
| description | Complete constructor signatures and method signatures for all skforecast forecasters, backtesting functions, search functions, cross-validation classes, preprocessing, feature selection, and drift detection. Use when the user needs exact parameter names, types, or defaults for any skforecast class or function. |
Use this when you need exact parameter names, types, defaults, or method signatures for any skforecast class or function.
This skill contains the full constructor and method signatures for all public skforecast classes and functions. See references/method-signatures.md for the complete reference, including:
fit(), predict(), predict_interval(), predict_quantiles(), predict_dist() signaturesset_params(), set_lags(), set_out_sample_residuals() signaturesForecasterRecursive — single series, recursive strategyForecasterRecursiveMultiSeries — multiple series, global modelForecasterDirect — single series, one model per stepForecasterDirectMultiVariate — multiple input series, one targetForecasterRecursiveClassifier — classification-basedForecasterStats — statistical models (ARIMA, ETS, SARIMAX, ARAR)ForecasterEquivalentDate — baseline using past offsetsForecasterRnn — deep learning (RNN/LSTM/GRU)ForecasterFoundation — zero-shot with foundation models (Chronos-2, TimesFM 2.5, Moirai-2, TabICL)FoundationModel — low-level foundation model wrapper used by ForecasterFoundationfit() — train the modelpredict() — generate point forecastspredict_interval() — generate prediction intervalsbacktesting_forecaster — backtest single-series forecastersbacktesting_forecaster_multiseries — backtest multi-series forecastersbacktesting_stats — backtest statistical modelsgrid_search_forecaster / grid_search_forecaster_multiseries / grid_search_statsrandom_search_forecaster / random_search_forecaster_multiseries / random_search_statsbayesian_search_forecaster / bayesian_search_forecaster_multiseriesTimeSeriesFold — multi-step cross-validationOneStepAheadFold — fast one-step cross-validationselect_features — single seriesselect_features_multiseries — multi-seriesRangeDriftDetector — lightweight range checkPopulationDriftDetector — statistical testsRollingFeatures — rolling window statisticsTimeSeriesDifferentiator — differencingDateTimeFeatureTransformer — calendar featuresCreates features for time series forecasting: calendar features with feature_engine (DatetimeFeatures, CyclicalFeatures), rolling statistics with RollingFeatures, differencing, and categorical exogenous variables. Use when the user wants to improve model accuracy through feature engineering or asks about exogenous variable creation.
Optimizes forecaster hyperparameters using grid search, random search, or Bayesian search (Optuna). Covers single-series and multi-series search, cross-validation configuration, and search space definition. Use when the user wants to find the best model configuration.
Diagnoses and fixes common errors when using skforecast, especially mistakes frequently made by LLMs generating skforecast code. Covers deprecated imports, wrong function names, missing parameters, and data format issues. Use when generated code produces errors or unexpected results.
Guides selection of the appropriate skforecast forecaster based on the user's data characteristics and requirements. Provides a decision matrix mapping use cases to forecaster classes. Use when the user is unsure which forecaster to use or asks for a recommendation.
Zero-shot time series forecasting with pre-trained foundation models (Amazon Chronos-2, Google TimesFM 2.5, Salesforce Moirai-2, Soda-INRIA TabICL) via ForecasterFoundation and FoundationModel. Covers single and multi-series workflows, exogenous variables, prediction intervals / quantiles, and backtesting. Use when the user wants forecasts without task-specific training, cold-start baselines, or pre-trained generalist models.
Generates prediction intervals for time series forecasts using bootstrapping, conformal prediction, or built-in statistical model intervals. Covers interval configuration, residual management, and calibration. Use when the user needs uncertainty quantification for forecasts.