DelayCast: Event-Gated Low-Rank Residual Adaptation for Time-Series Foundation Models

Time-series foundation models achieve strong forecasting performance from historical numerical observations, while external event notifications may provide additional information about future changes. Existing approaches incorporate external information through numerical covariates or multimodal fusion, yet effectively utilizing sparsely released textual notifications remains challenging when their availability and subsequent effects are temporally uncertain. To address this challenge, we propose DelayCast, an event-gated low-rank residual adaptation framework that incorporates structured information extracted from event notifications into frozen foundation-model forecasts. DelayCast combines historical numerical states with cutoff-available event attributes to generate constrained multi-horizon residual corrections, while preserving the original backbone forecast when no eligible event information is available. On the SpaceWeather dataset, DelayCast reduces the 72-hour forecasting mean absolute error of frozen Chronos-2 by 8.97% on the 2025 evaluation and 3.50% on a partial-year 2026 cohort. Beyond forecasting performance, we employ hierarchical comparisons to distinguish numerical calibration from the incremental contributions of event information, and further investigate the gap between the predictive value of retrospectively known response timing and the ability to recover such temporal structure from sparse event supervision.