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

The article discusses the development of an end-to-end forecasting pipeline using TimeCopilot, which incorporates foundation models and automated anomaly detection techniques. It evaluates various models, including statistical and GPU-based approaches, through rolling cross-validation, generating probabilistic forecasts with prediction intervals and anomaly flagging. The inclusion of a TimeCopilot LLM agent for model selection and prediction explanation enhances the interpretability of forecasts, making this pipeline valuable for practitioners focused on robust forecasting solutions in AI applications.