这教程用TimesFM 2.5把预测全流程都走了一遍，回测、协变量、异常检测都有，跟着能直接在Colab跑起来。
该教程基于TimesFM 2.5构建完整的时序预测工作流，覆盖配置运行时、安装依赖、硬件检测和生成多门店零售数据集。数据集包含趋势、季节性、价格、促销、节假日和温度效应等真实因素。流程中详细演示了回测、协变量处理和异常检测方法，并支持在Colab中直接部署可扩展的预测管线。
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 […] The post End-to-End Forecasting with TimesFM 2.5: Backtesting, Covariates, Anomaly Detection, and Scalable Colab Deployment appeared first on MarkTechPost .