End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
Summary
<p>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 […]</p> <p>The post <a href="https://www.marktechpost.com/2026/08/01/end-to-end-forecasting-with-timesfm-2-5-backtesting-covariates-anomaly-detection-and-scalable-colab-deployment/">End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>