[2509.14181] Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting

Computer Science > Machine Learning arXiv:2509.14181 (cs) [Submitted on 17 Sep 2025 (v1), last revised 25 Mar 2026 (this version, v4)] Title:Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting Authors:Yifan Hu, Jie Yang, Tian Zhou, Peiyuan Liu, Yujin Tang, Rong Jin, Liang Sun View a PDF of the paper titled Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting, by Yifan Hu and 6 other authors View PDF HTML (experimental) Abstract:Although contrastive and other representation-learning methods have long been explored in vision and NLP, their adoption in modern time series forecasters remains limited. We believe they hold strong promise for this domain. To unlock this potential, we explicitly align past and future representations, thereby bridging the distributional gap between input histories and future targets. To this end, we introduce TimeAlign, a lightweight, plug-and-play framework that establishes a new representation paradigm, distinct from contrastive learning, by aligning auxiliary features via a simple reconstruction task and feeding them back into any base forecaster. Extensive experiments across eight benchmarks verify its superior performance. Further studies indicate that the gains arise primarily from correcting frequency mismatches between historical inputs and future outputs. Additionally, we provide two theoretical justifications for how reconstruction improves forecasting generalization and how ali...