A new tutorial has been published that outlines the steps to build an advanced end-to-end time-series forecasting workflow using TimesFM 2.5. The tutorial begins by setting up the necessary environment and dependencies, followed by the generation of a realistic retail dataset with various influencing factors. The model is then loaded and compiled, and its performance can be examined. The workflow also includes features for backtesting, anomaly detection, and scalable deployment in Colab. This development is significant as it provides a comprehensive framework for businesses and organizations to improve their forecasting capabilities.