Configures TimeSeriesFold parameters for backtesting based on deployment scenarios. Maps business requirements (retraining frequency, forecast horizon, data budget) to cross-validation strategy parameters. Use when the user describes how they plan to deploy or evaluate a model.
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.
Forecasts time series using recurrent neural networks (RNN, LSTM, GRU) with ForecasterRnn and the create_and_compile_model helper. Covers model architecture, training, and multi-series deep learning. Use when the user wants to use deep learning / neural networks for time series forecasting.
Creates features for time series forecasting: calendar features with skforecast's `CalendarFeatures` (cyclical, onehot, or spline encoding) — either delegated to the forecaster via the `calendar_features` parameter or built manually as exog — holiday distance features with `calculate_distance_from_holiday`, 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.
Forecasts multiple time series simultaneously using a global model with ForecasterRecursiveMultiSeries or ForecasterDirectMultiVariate. Covers data formats, encoding, per-series transformers, and multi-series backtesting. Use when the user has two or more related time series.
Forecasts a single time series using ForecasterRecursive or ForecasterDirect. Covers data preparation, model creation, training, prediction, backtesting, and prediction intervals. Use when the user needs to predict future values of one time series.
Zero-shot time series forecasting with pre-trained foundation models (Amazon Chronos-2, Google TimesFM 2.5, Salesforce Moirai-2, Soda-INRIA TabICL, Prior Labs TabPFN-TS, The Forecasting Company T0) 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.
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.