Unsupervised Time Series Domain Adaptation (UTSDA) is a method for transferring information from a labeled source domain to an unlabeled target domain. The majority of existing UTSDA approaches focus on learning a domain-invariant feature space by reducing the gap between domains. However, the single-task representation learning methods have limited expressive capability, while ignoring the distinctive season-related and trend-related domain-invariant mechanisms across different domains. To address this, we introduce a novel approach, distinct from existing methods, through a theoretical analysis of UTSDA from the perspective of causal inference. This analysis establishes a solid theoretical foundation for identifying and modeling such consistent domain-invariant mechanisms, which is a significant advancement in the field. As a solution, we introduce MDLR, a multi-task disentangled learning framework designed for UTSDA. MDLR utilizes a dual-tower architecture with a trend feature extractor (TFE) and a season feature extractor (SFE) to extract trend-related and season-related information. This approach ensures that domain-invariant features at different scales can be better represented. Additionally, MDLR is designed with two tasks: a label classifier and a domain classifier, enabling iterative training of the entire model. The experiments conducted on three datasets, namely UCIHAR, WISDM, and HHAR_SA, along with visualization results, have shown the effectiveness of the proposed approach. The source code for our MDLR model is available to the public at https://github.com/MoranCoder95/MDLR/.

无监督时间序列域适应（UTSDA）是一种将信息从标记的源域传输到未标记的目标域的方法。大多数现有的 UTSDA 方法侧重于通过减少域之间的差距来学习域不变的特征空间。然而，单任务表示学习方法的表达能力有限，同时忽略了跨不同领域的独特的季节相关和趋势相关的领域不变机制。为了解决这个问题，我们通过从因果推理的角度对 UTSDA 进行理论分析，引入了一种不同于现有方法的新颖方法。该分析为识别和建模这种一致的域不变机制奠定了坚实的理论基础，这是该领域的重大进步。作为解决方案，我们引入了 MDLR，这是一个专为 UTSDA 设计的多任务解耦学习框架。MDLR 利用带有趋势特征提取器 (TFE) 和季节特征提取器 (SFE) 的双塔架构来提取趋势相关和季节相关信息。这种方法确保可以更好地表示不同尺度的域不变特征。此外，MDLR 设计有两个任务：标签分类器和域分类器，从而实现整个模型的迭代训练。在 UCIHAR、WISDM 和 HHAR_SA 三个数据集上进行的实验以及可视化结果表明了该方法的有效性。我们的 MDLR 模型的源代码可在https://github.com/MoranCoder95/MDLR/向公众开放。