How to Build a Forecasting Pipeline with TimeCopilot Using Foundation Models and Automated Anomaly Detection

Summary

<p>We build an end-to-end forecasting workflow with TimeCopilot on a panel of real airline passenger data and a synthetic seasonal series with injected anomalies. We evaluate statistical, foundation, and optional GPU-based models using rolling cross-validation and multiple error metrics. We generate probabilistic forecasts with prediction intervals, visualize future trends, and flag unusual observations. We then explore TimeCopilot's optional LLM agent, which selects a model and explains its predictions.</p> <p>The post <a href="https://www.marktechpost.com/2026/06/20/how-to-build-a-forecasting-pipeline-with-timecopilot-using-foundation-models-and-automated-anomaly-detection/">How to Build a Forecasting Pipeline with TimeCopilot Using Foundation Models and Automated Anomaly Detection</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.</p>