Winformer: Transcending Pairwise Similarity for Time-series Generation

Abstract

The periodicity misalignment remains a challenge problem in generating time-series data across multiple domains. Existing methods model time-series interactions either at the granularity of individual points or fragmented segments. This limits their ability to capture and adapt to complex periodic patterns inherent in diverse domains. To address this, we introduce Winformer, a novel diffusion framework built on window-wise attention mechanism. We shift the fundamental processing unit in the attention mechanism from pairwise points similarity to continuous windows comparison of the entire horizon. 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.