REIN: Residual Modulation of Time Series Forecasts via LLM-Refined News Regime Signals

Time series forecasts in operational domains are often disrupted by external events such as heatwaves, generator outages, policy interventions, supply shocks, and market incidents. Most deep forecasting models rely on numerical histories, while recent text-augmented methods offer limited control over how far text can move a forecast, which temporal component text should influence, and whether observed gains reflect time-aligned news content rather than news availability or calendar correlations. We introduce REIN (Residual Information from News), a two-stage framework that reformulates news-aware forecasting as bounded residual modulation. Our design leverages an LLM agent to distill raw news records into a leakage-controlled regime bank of compact, time-aligned signals. We further incorporate a PatchTST-based delta module to decompose the residual of a frozen base forecaster into persistent-level, intra-day-shape, and extreme-event-spike channels. Following our design principles, news does not write forecasts directly. It only scales these residual channels through a small set of bounded modulation knobs. This construction enforces a bound on deviation from the base forecast and provides a clean reversion path when the news bank is empty. A counterfactual protocol with news blanking and date permutation, evaluated on disjoint news-active and news-inactive subsets, provides sample-level evidence that improvements are tied to selective use of aligned news. On four heavy-tailed real-world datasets (Electricity Demand, Electricity Price, Traffic, Gas Demand) at horizons up to 720 steps, REIN improves MAE by 2–21% over a strong DLinear base and presents state-of-the-art performance on most settings. Blanking the news increases news-active MAE on all four datasets, by up to 42.6% on the most news-sensitive setting (Traffic, $H=96$), supporting the view that REIN benefits from structured news signals rather than from adding text indiscriminately.