Abstract
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.
Abstract (translated)
时间序列生成模型对于数据增强和隐私保护等应用至关重要。现有的大多数时间序列生成模型通常被设计用于从特定领域生成数据。尽管在其他应用程序中，利用来自不同领域的数据以实现更好的泛化能力已被证明有效，但在时间序列建模中，由于不同的现实世界时间序列类别之间模式的显著差异，这种方法仍然具有挑战性。 本文提出了一种使用领域提示的多域时间序列扩散模型，命名为TimeDP。在TimeDP中，我们采用了一个时间序列语义原型模块，该模块定义了代表时间序列基础的时间序列原型，每个原型向量作为“词”，表示某些基本的时间序列特征。一个原型分配模块被用来提取特定领域的原型权重，以学习生成条件的领域提示。在采样过程中，我们从目标领域中使用少量样本抽取“领域提示”并将其用作生成时间序列样本的条件。 实验表明，与基线方法相比，我们的方法不仅提供了最先进的域内生成质量，还具备强大的未见领域生成能力。
URL
https://arxiv.org/abs/2501.05403
