摘要： 时间序列分割是时间序列分析中的一项重要任务，在生物行为分析和物理系统分析等领域得到了广泛的应用。然而，现有的时间序列分割方法普遍忽略了分布偏移下时间序列的非稳态特性，导致模型在非稳态时间序列中难以实现精准分割。为了解决这个问题，首先，提出一个基于现实场景的数据因果生成过程假设。在该假设下，观测数据背后的隐变量可分为稳态隐变量和非稳态隐变量，其中稳态变量表示不变或周期性变化的信息，非稳态变量表示动态变化的信息。其次，基于该因果生成过程假设，设计了稳态非稳态解耦模型，该模型将稳态和非稳态变量进行解耦，可以更加关注时间序列中的非稳态依赖关系。此外，为了准确解耦与提取变量，利用变分推断的证据下界（ELBO）来构造模型的损失函数，并基于该证据下界，通过稳态和非稳态先验神经网络模块来提高隐变量解耦的准确性。最后，通过实验证明，在各种基准数据集上，该模型的性能优于几种最新的时间序列分割方法，突显了其在实际场景中的优势。

Abstract: Time series segmentation, an important task in time series analysis, has been widely applied in fields such as biological behavior analysis and physical system analysis. However, most existing time series segmentation methods fail to account for the nonstationary dynamics of time series induced by distribution shifts, thereby limiting their ability to achieve accurate segmentation in nonstationary regimes. To solve this problem, this paper first proposes a data causal generation process hypothesis based on real-world scenarios. Under this hypothesis, the latent variables underlying the observed data can be decomposed into stationary and non-stationary latent variables. Here, the stationary variables represent information that is unchanged or changes periodically, while the nonstationary variables represent dynamically changing information. Secondly, based on this causal generation process hypothesis, a Stationary Nonstationary Disentangle Model (SNDM) is designed. This model disentangles stationary and nonstationary variables, thus enabling enhanced focus on non-stationary dependencies in the time series. Moreover, in order to accurately disentangle and extract variables, the evidence lower bound (ELBO) of variational inference is used to construct the loss function of the model. Leveraging this ELBO, this study introduces stationary and nonstationary prior neural network modules to improve latent variable disentanglement accuracy. Finally, through experiments, we validate that our model outperforms several state-of-the-art time series segmentation methods on various benchmark datasets, thereby highlighting its advantages in practical scenarios.