READING THE DATE, NOT THE EVENT: THE CALENDAR SHORTCUT IN TEXT-ASSISTED TIME SERIES FORECASTING

Text-assisted forecasting pairs numerical windows with contemporaneous documents to capture event semantics unavailable from the series alone. Evidence for this premise rests largely on modality ablation (adding text helps, removing it hurts), which establishes predictive signal but does not identify its source. We design controlled interventions on trained forecasters to localize that signal: reversing event semantics alters MAE by less than 1%, whereas masking only dates and timestamp expressions reproduces at least 74% of the full text removal penalty in five of the six settings. We term this mode the calendar shortcut: forecasters recover periodic phase from textual calendar cues while largely bypassing event content. To overcome it, we propose State Conditioned Paired Residual Adaptation (SPRA), a lightweight, backbone agnostic framework for state-conditioned residual learning. Shortcut controlled document pairs preserve event content while suppressing calendar cues, and a paired consistency objective reduces the adapter's reliance on such cues. A low-rank adapter then estimates what the text contributes beyond the observed trajectory and maps it to a target specific multi-horizon residual correction of a frozen backbone. Across nine domains and five backbones, SPRA ranks first in 79 of 90 comparisons, improves upon its unimodal backbone in 44 of 45 settings, and retains its gains under the same shortcut interventions, supporting attribution to forecast-relevant event content rather than incidental calendar identity.