Winformer: Transcending Pairwise Similarity for Time-series Generation

The periodicity misalignment remains a challenge problem in generating time-series data across multiple domains. The fundamental processing unit of attention in time-series modeling has long been restricted to either individual points or fragmented segments, limiting their ability to capture and adapt to complex periodic patterns inherent in diverse domains. To address this, we introduce Winformer, first to extend this processing unit from individual points to sliding windows, establishing a unified window-wise attention paradigm. Leveraging the adaptive window-alignment kernels derived from the frequency decomposition, Winformer brings semantically richer window representations, and effectively captures and transfers complex periodic patterns across domains. Extensive experiments on 12 real-world datasets demonstrate Winformer's effectiveness, achieving an average performance gain of 10.67% over SOTA baselines.