forecasting

Predict the per-feature training mean for every observation.

One-step-ahead naive seasonal forecasts backed by `darts`.

Base for deterministic local forecasts with explicit horizons.

Fit an autoregression with jointly modeled components and ridge regularization.

Extrapolate the line joining the first and last context observation.

Repeat the fitted per-component mean for every forecast step.

Repeat the last ``K`` observations; ``K=1`` is persistence.

Estimate expected values from a rolling window and score residuals.

Train any PyTorch module on chronological time-series windows.