skforecast/skforecast-ai/complete-api-reference

Complete API Reference

When to Use

Use this when you need exact parameter names, types, defaults, or method signatures for any skforecast class or function.

Related skills

• Prerequisite: choosing-a-forecaster (pick the class first; this skill only supplies its signature)
• Alongside: any workflow skill (they show the idiomatic usage, this one the exact arguments)
• Next: troubleshooting-common-errors (when a signature looks right but the call still fails)

Quick Index

Forecaster Constructors

• ForecasterRecursive — single series, recursive strategy
• ForecasterRecursiveMultiSeries — multiple series, global model
• ForecasterDirect — single series, one model per step
• ForecasterDirectMultiVariate — multiple input series, one target
• ForecasterRecursiveClassifier — classification-based
• ForecasterStats — statistical models (ARIMA, ETS, SARIMAX, ARAR)
• ForecasterEquivalentDate — baseline using past offsets
• ForecasterRnn — deep learning (RNN/LSTM/GRU)
• ForecasterFoundation — zero-shot with foundation models (Chronos-2, TimesFM 2.5/3.0, Moirai-2, TabICL, TabPFN-TS, TFC-T0, Nori, TS-ICL)
• FoundationModel — low-level foundation model wrapper used by ForecasterFoundation
• get_model_info / list_adapters: capabilities of a foundation model (exog, quantiles, context length, backend, license) without installing its backend

Forecaster Methods

• fit() — train the model
• predict() — generate point forecasts
• predict_interval() — generate prediction intervals

Model Selection

• backtesting_forecaster — backtest single-series forecasters
• backtesting_forecaster_multiseries — backtest multi-series forecasters
• backtesting_stats — backtest statistical models
• backtesting_foundation — backtest ForecasterFoundation (zero-shot)
• grid_search_forecaster / grid_search_forecaster_multiseries / grid_search_stats
• random_search_forecaster / random_search_forecaster_multiseries / random_search_stats
• bayesian_search_forecaster / bayesian_search_forecaster_multiseries
• bayesian_search_foundation — tune ForecasterFoundation inference-time parameters
• grid_search_equivalent_date — tune ForecasterEquivalentDate baselines
• TimeSeriesFold — multi-step cross-validation
• OneStepAheadFold — fast one-step cross-validation

Feature Selection

• select_features — single series
• select_features_multiseries — multi-series

Drift Detection

• RangeDriftDetector — lightweight range check
• PopulationDriftDetector — statistical tests

Preprocessing

• RollingFeatures — rolling window statistics
• TimeSeriesDifferentiator — differencing
• CalendarFeatures — calendar features

References

Full constructor and method signatures for all public skforecast classes and functions, split by domain:

• Forecaster constructors: See references/forecaster-constructors.md
• Forecaster methods (fit(), predict(), predict_interval(), predict_quantiles(), predict_dist(), set_params(), set_lags(), set_out_sample_residuals(), availability matrix): See references/forecaster-methods.md
• Backtesting, search, cross-validation, feature selection: See references/model-selection-signatures.md
• Drift detection and preprocessing: See references/preprocessing-signatures.md