Tutorial: Time Series Forecasting with TimesFM 2.5 - Backtesting, Covariates, and Anomaly Detection

A new technical tutorial presents an end-to-end time series forecasting workflow using TimesFM 2.5. The tutorial demonstrates practical applications including backtesting across multiple historical cutoffs, integration of numerical and categorical covariates, and anomaly detection based on prediction intervals. The workflow begins with Google Colab environment configuration, hardware detection, and generation of a realistic multi-store retail dataset incorporating trends, seasonality, pricing effects, promotions, holidays, and temperature variations. The model is compiled for zero-shot forecasting with point and probabilistic predictions. The pipeline evaluates quality through metrics including MAE, RMSE, sMAPE, MASE, and pinball loss while testing batched inference, rolling-origin backtesting, context-length sensitivity, and covariate integration via XReg. Ablation studies determine how different amounts of historical data influence accuracy and interval coverage. Anomaly detection compares observed values against the model's quantile intervals, assigning severity levels. The tutorial demonstrates capability to identify spikes, outages, and sustained shifts in retail series.