Time series domain adaptation aims to transfer the complex temporal dependence from the labeled source domain to the unlabeled target domain. Recent advances leverage the stable causal mechanism over observed variables to model the domain-invariant temporal dependence. However, modeling precise causal structures in high-dimensional data, such as videos, remains challenging. Additionally, direct causal edges may not exist among observed variables (e.g., pixels). These limitations hinder the applicability of existing approaches to real-world scenarios. To address these challenges, we find that the high-dimension time series data are generated from the low-dimension latent variables, which motivates us to model the causal mechanisms of the temporal latent process. Based on this intuition, we propose a latent causal mechanism identification framework that guarantees the uniqueness of the reconstructed latent causal structures. Specifically, we first identify latent variables by utilizing sufficient changes in historical information. Moreover, by enforcing the sparsity of the relationships of latent variables, we can achieve identifiable latent causal structures. Built on the theoretical results, we develop the Latent Causality Alignment (LCA) model that leverages variational inference, which incorporates an intra-domain latent sparsity constraint for latent structure reconstruction and an inter-domain latent sparsity constraint for domain-invariant structure reconstruction. Experiment results on eight benchmarks show a general improvement in the domain-adaptive time series classification and forecasting tasks, highlighting the effectiveness of our method in real-world scenarios. Codes are available at https://github.com/DMIRLAB-Group/LCA.

翻译：时间序列域适应的目标是将带标签源域中的复杂时间依赖性迁移至无标签目标域。近期研究利用观测变量上的稳定因果机制来建模域不变的时间依赖性。然而，在高维数据（如视频）中建模精确的因果结构仍具挑战性。此外，观测变量（例如像素）之间可能不存在直接的因果边。这些限制阻碍了现有方法在现实场景中的适用性。为解决这些挑战，我们发现高维时间序列数据由低维潜在变量生成，这促使我们对时序潜在过程的因果机制进行建模。基于这一思路，我们提出了一个保证重构潜在因果结构唯一性的潜在因果机制识别框架。具体而言，我们首先通过利用历史信息的充分变化来识别潜在变量。此外，通过强制潜在变量关系的稀疏性，我们可以实现可识别的潜在因果结构。基于理论结果，我们开发了利用变分推理的潜在因果对齐（LCA）模型，该模型结合了用于潜在结构重构的域内潜在稀疏性约束和用于域不变结构重构的域间潜在稀疏性约束。在八个基准测试上的实验结果表明，在域适应时间序列分类和预测任务中普遍取得了改进，突显了我们方法在现实场景中的有效性。代码可在 https://github.com/DMIRLAB-Group/LCA 获取。