News-Aware Time Series Forecasting Agent Enhanced by Offline Reinforcement Learning

Accurate time-series forecasting in event-sensitive environments can benefit from external news, but semantically relevant news is not necessarily useful for forecasting. We study news use as a forecasting-feedback-driven selection problem. Our framework combines an LLM-based forecaster with a sequential selector that decides whether each candidate article should be kept, dropped, or summarized, conditioned on the numerical history, structured context, and information already retained. The selector is learned from fixed historical trajectories using offline reinforcement learning, with terminal reward defined by improvement in downstream forecasting loss relative to a no-news forecast. We further construct a forecasting-error reflection memory that retains only counterfactual selection corrections that reduce historical forecasting loss. We evaluate the framework on electricity demand, AUD/USD, and Bitcoin forecasting. End-to-end results show that the usefulness of external news and reflection varies across tasks: electricity favors highly conservative information use, whereas selected news provides larger benefits in the financial tasks. In a controlled fixed-forecaster evaluation, changing only the supplied news context materially affects forecasting accuracy, and the best forecasting-feedback-selected context achieves lower RMSE than both no-news and LLM-selected contexts across all three domains. These results show that forecasting utility should be learned from downstream forecasting feedback rather than inferred from semantic relevance alone.