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

MarkTechPost published a tutorial on building a forecasting pipeline with TimeCopilot, using foundation models and automated anomaly detection on airline passenger data and synthetic seasonal series. The workflow includes rolling cross-validation, probabilistic forecasts, and an optional LLM agent for model selection and explanation.

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. The post How to Build a Forecasting Pipeline with TimeCopilot Using Foundation Models and Automated Anomaly Detection https://www.marktechpost.com/2026/06/20/how-to-build-a-forecasting-pipeline-with-timecopilot-using-foundation-models-and-automated-anomaly-detection/ appeared first on MarkTechPost https://www.marktechpost.com .