Time series generation models are crucial for applications like data augmentation and privacy preservation. Most existing time series generation models are typically designed to generate data from one specified domain. While leveraging data from other domain for better generalization is proved to work in other application areas, this approach remains challenging for time series modeling due to the large divergence in patterns among different real world time series categories. In this paper, we propose a multi-domain time series diffusion model with domain prompts, named TimeDP. In TimeDP, we utilize a time series semantic prototype module which defines time series prototypes to represent time series basis, each prototype vector serving as "word" representing some elementary time series feature. A prototype assignment module is applied to extract the extract domain specific prototype weights, for learning domain prompts as generation condition. During sampling, we extract "domain prompt" with few-shot samples from the target domain and use the domain prompts as condition to generate time series samples. Experiments demonstrate that our method outperforms baselines to provide the state-of-the-art in-domain generation quality and strong unseen domain generation capability.

本研究解决了现有时间序列生成模型只能在单一领域生成数据的限制，提出了一种新的多领域时间序列扩散模型TimeDP。该模型引入时间序列语义原型模块和原型赋值模块，通过少量目标领域样本生成时间序列，实验证明该方法在领域内生成质量和未见领域生成能力方面表现优异。

TimeDP：学习生成多领域时间序列的领域提示