End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
TimesFM 2.5 tutorial: backtesting, anomaly detection, and covariate handling in a single workflow.
Why it matters
A practical guide to deploying Google's time-series forecasting model in production. Relevant for practitioners building demand planning, inventory, or financial forecasting systems who want to move beyond baseline models.
The key facts
13 to knowTimesFM 2.5 model
End-to-end workflow with backtesting
Covariate integration (pricing, promotions, holidays, temperature)
Anomaly detection capability
Scalable Colab deployment
Multi-store retail dataset example
Published as tutorial/how-to guide
TimesFM 2.5 model release
End-to-end forecasting workflow tutorial
Backtesting, covariate handling, anomaly detection capabilities
Scalable Colab deployment (no local GPU required)
Multi-store retail dataset with trend, seasonality, pricing, promotions, holidays, temperature effects
Google AI model applied to time-series forecasting
Go to the source
MarkTechPostmarktechpost.com
Publisher excerpt: 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,…