CastFSR: A Fast–Slow–Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting
The episode explores CastFSR, a framework for time series forecasting that moves beyond simple pattern matching. It uses an agentic approach to predict future values by incorporating external factors like weather or holidays. The process involves three stages: a fast numerical guess, slow contextual reasoning, and a final reflection check against reality to produce accurate and plausible forecasts.