发表机构
ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出在小波域进行流匹配以生成多变量时间序列，通过多层小波系数实现隐式从粗到细的生成，并结合通道令牌变换器建模跨通道依赖，在多个基准上取得最优或并列最优结果。
AI 中文摘要
合成时间序列越来越多地用于数据增强、隐私保护数据共享和下游模型开发，然而忠实再现多尺度时间结构和跨通道依赖性仍然具有挑战性。我们通过小波域中的流匹配来研究多变量时间序列生成。通过在多层离散小波系数上操作，而非直接在时间域中操作，该模型在单独的尺度上表示粗结构和逐步更精细的细节。它们自然不同的方差进一步诱导了隐式的从粗到细的生成过程，而无需显式的多尺度调度。由于变换独立作用于每个通道，我们将其与通道令牌变换器配对，其注意力直接建模跨通道依赖性。在七个基准数据集和四种序列长度上，我们的方法在大多数数据集-指标组合中达到最佳或并列最佳，其中在Context-FID和判别性得分方面取得了最大且最一致的改进。
英文摘要
Synthetic time series are increasingly used for data augmentation, privacy-preserving data sharing, and downstream model development, yet faithfully reproducing both multi-scale temporal structure and cross-channel dependencies remains challenging. We study multivariate time-series generation through flow matching in the wavelet domain. By operating on multilevel discrete wavelet coefficients rather than directly in the time domain, the model represents coarse structure and progressively finer details at separate scales. Their naturally different variances further induce an implicit coarse-to-fine generative process without requiring an explicit multi-scale schedule. Since the transform acts independently on each channel, we pair it with a channel-token transformer whose attention directly models cross-channel dependencies. Across seven benchmark datasets and four sequence lengths, our method is best or tied on a majority of dataset-metric combinations, with the largest and most consistent improvements in Context-FID and discriminative score.
Comments45 pages, including appendix; 11 figures, 13 tables
