End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment

Short summary

This tutorial walks through an end-to-end time-series forecasting pipeline using TimesFM 2.5, covering runtime configuration, dependency installation, and hardware detection on Google Colab. It generates a synthetic multi-store retail dataset incorporating trend, seasonality, pricing, promotions, holidays, and temperature effects, then loads and compiles the TimesFM 2.5 model for backtesting, covariate handling, and anomaly detection. The workflow is designed for scalable deployment in a Colab environment, making it practical for analysts and operations teams building production-grade forecasting systems.

• •End-to-end TimesFM 2.5 tutorial covering backtesting, covariates, and anomaly detection
• •Uses a synthetic multi-store retail dataset with trend, seasonality, pricing, and holiday effects
• •Designed for scalable deployment on Google Colab with hardware auto-detection

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