When Events Move the Curve: An Event-Informed Reasoning Framework for Multimodal Time Series Forecasting

Strong time series forecasting models can effectively capture endogenous patterns from numerical histories, but they cannot directly access the external events described in text. Existing multimodal methods either compress text into latent features, underutilizing event semantics, or rely on LLMs to directly generate numerical forecasts, risking imprecision due to the language–number modality gap. Consequently, they struggle to retain the forecasting capability of numerical models while fully exploiting textual event semantics and accurately translating them into future numerical changes. We propose After (Adjustment Framework through Textual Event Reasoning), an event-informed framework that retains a trained numerical backbone and uses textual events to adjust its predictions without additional parameter optimization. After first employs History-Aware Event Reasoning to identify forecast-relevant events from the textual context. It then derives forecast offsets through contextual event reasoning, case-based analogical reasoning over an Event-Response Memory, and direct numerical transfer of retrieved continuations. These complementary offset candidates are robustly integrated and added to the backbone forecast. Experiments across nine real-world multimodal time series datasets and multiple forecasting horizons demonstrate the effectiveness and broad applicability of After against strong numerical and multimodal baselines. Our code is available at https://anonymous.4open.science/r/AFTER-ICLR–0638/