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

ArXi:2509.14181v4 Announce Type: replace 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