End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
MarkTechPost Sana Hassan
Google's TimesFM 2.5 model is demonstrated in an end-to-end time-series forecasting tutorial using a synthetic multi-store retail dataset with 1,200 days of data across 6 stores. The tutorial evaluates TimesFM's performance using metrics including MAE, RMSE, sMAPE, MASE, and pinball loss, with a 56-day forecast horizon and rolling-origin backtesting across 6 folds. Results show TimesFM beats seasonal naive baselines and enables batch inference across multiple series while supporting probabilistic quantile forecasts, covariate integration, anomaly detection, and uncertainty quantification through prediction intervals.
Why it matters
In this tutorial, we build an advanced end-to-end time-series forecasting workflow with TimesFM 2.5. We begin by configuring the runtime, installing the required dependencies, detecting available hardware, and generating a realistic multi-store retail dataset with trend, seasonality, pricing, promotions, holidays, temperature effects, and random variation. We then load and compile the TimesFM 2.5 model, examine […] The post End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment appeared first on MarkTechPost.
