Time series generation is widely used in real-world applications such as simulation, data augmentation, and hypothesis testing. Recently, diffusion models have emerged as the de facto approach to time series generation, enabling diverse synthesis scenarios. However, the fixed standard-Gaussian diffusion prior may be ill-suited for time series data, which exhibit properties such as temporal order and fixed time points. In this paper, we propose TimeBridge, a framework that flexibly synthesizes time series data by using diffusion bridges to learn paths between a chosen prior and the data distribution. We then explore several prior designs tailored to time series synthesis. Our framework covers (i) data- and time-dependent priors for unconditional generation and (ii) scale-preserving priors for conditional generation. Experiments show that our framework with data-driven priors outperforms standard diffusion models on time series generation.

翻译：时间序列生成在仿真、数据增强和假设检验等现实应用中具有广泛用途。近年来，扩散模型已成为时间序列生成的事实标准方法，能够实现多样化的合成场景。然而，固定的标准高斯扩散先验可能不适用于具有时序性和固定时间点等特性的时间序列数据。本文提出TimeBridge框架，该框架通过使用扩散桥学习选定先验与数据分布之间的路径，从而灵活合成时间序列数据。我们进一步探索了多种针对时间序列合成定制的先验设计。该框架涵盖：（i）用于无条件生成的数据依赖及时变先验；（ii）用于条件生成的尺度保持先验。实验表明，采用数据驱动先验的本框架在时间序列生成任务上优于标准扩散模型。