Time series and forecasting · insight-0207

Choose expanding or sliding training windows by backtest, not by habit

Situation
A logistics volume series changed behaviour after a network redesign two years ago, and the model trains on all history.
Action
Backtest both an expanding window and two or three sliding window lengths over recent origins, and pick the one with the best and most stable error on the horizons you use.
Reason
Long windows reduce estimation variance; short windows reduce bias from outdated relationships. Which effect dominates is an empirical question for each series.
Boundary
Series with suspected drift, regime changes, or long histories.

Symptom or risk

Old regime data pull forecasts toward patterns that no longer exist; a sliding window may track the new level better but is noisier.

Rare seasonal events (annual holidays) need enough years in the window to be learned at all.

Sliding windows forget useful history; sample weights that decay with age are a middle path.

Choosing the window on the same final block you report as test accuracy.

For very long series, a remedy for model misspecification is to fit a model only to the most recent observations.
Forecasting: Principles and Practice (3rd edition)