End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment
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 its forecast configuration, and use it for zero-shot point and probabilistic forecas
This tutorial covers an end-to-end workflow for time-series forecasting using TimesFM 2.5, a machine learning model designed to predict future values in sequential data. The workflow includes setting up the model, generating realistic retail data with multiple factors like trends and seasonal patterns, and then evaluating forecast accuracy through various metrics and testing methods such as backtesting, anomaly detection, and covariate integration. The tutorial demonstrates practical techniques for configuring, validating, and deploying the model across different scenarios and data complexities.
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Also reported by The Verge, SiliconANGLE and MarkTechPost
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Also reported by SiliconANGLE
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