Context2Action: An Agentic Framework for Real-World-Aware Time-Series Forecasting

Time series are shaped not only by their historical dynamics but also by real-world contexts such as holidays, promotions, weather, news, and unexpected disruptions. Existing methods either focus primarily on numerical patterns or incorporate contextual information through end-to-end prediction, without explicitly determining how such contexts should influence forecasting decisions. We propose Context2Action, an agentic framework that translates real-world contexts into explicit and controllable forecasting actions. By analyzing the historical errors of numerical predictors, Context2Action learns how contextual factors are associated with both localized changes and broader temporal deviations. Retrieval-augmented LLM experts then adapt relevant historical experiences to the current forecasting state and generate structured forecast revisions, while a hierarchical decision mechanism filters unsupported actions. The framework is optimized through supervised fine-tuning and forecasting-oriented reinforcement learning, aligning contextual reasoning with numerical forecasting utility. Experiments on four context-rich datasets show that Context2Action outperforms strong numerical, LLM-based, and context-aware forecasting baselines. It also generalizes across heterogeneous numerical predictors without additional expert training, demonstrating its effectiveness and adaptability for real-world-context-aware time series forecasting.