SpecBridge: Future-Guided Spectral Alignment for Power Outage Forecasting

Reliable power outage forecasting is critical for grid resilience, emergency response, and resource allocation, particularly as outage events can impose substantial operational and economic consequences. However, outage dynamics vary substantially across time and geographic regions, making forecasting highly challenging. Existing time-series forecasting methods primarily learn a direct mapping from historical observations to future values, but often overlook how structural relationships encoded in the past evolve toward future temporal patterns, potentially compromising forecasting accuracy as outage patterns change. We introduce SpecBridge, a future-guided spectral alignment framework for power outage forecasting. SpecBridge constructs latent graphs from numerical and textual representations of historical observations, together with a future-time graph that serves as a reference during training. It aligns the source spectral bases to this reference and adaptively aggregates them using data-driven weights that account for both source-target structural discrepancy and graph density. The aggregated historical subspace and the future-time subspace jointly supervise the forecasting representation, complementing prediction and reconstruction objectives. Empirical evaluations across 11 state-level US power outage datasets demonstrate that our SpecBridge outperforms state-of-the-art baselines.