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

This tutorial demonstrates how to build an end-to-end forecasting workflow using TimeCopilot, a tool that combines statistical models, Prophet, and foundation models to predict future trends in time-series data. The workflow includes preparing datasets, evaluating multiple forecasting approaches through rolling cross-validation, identifying the strongest model by error metrics, and generating probabilistic forecasts with prediction intervals to quantify uncertainty. An optional LLM agent can automatically select the best forecasting model and translate its predictions into accessible analytical insights. This approach matters because it enables practitioners to systematically compare diverse forecasting techniques, detect anomalies in data, and produce both point estimates and confidence bounds for better decision-making.