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 appeared first on MarkTechPost.
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