The transfer of models trained on labeled datasets in a source domain to unlabeled target domains is made possible by unsupervised domain adaptation (UDA). However, when dealing with complex time series models, the transferability becomes challenging due to the dynamic temporal structure that varies between domains, resulting in feature shifts and gaps in the time and frequency representations. Furthermore, tasks in the source and target domains can have vastly different label distributions, making it difficult for UDA to mitigate label shifts and recognize labels that only exist in the target domain. We present RAINCOAT, the first model for both closed-set and universal DA on complex time series. RAINCOAT addresses feature and label shifts by considering both temporal and frequency features, aligning them across domains, and correcting for misalignments to facilitate the detection of private labels. Additionally,RAINCOAT improves transferability by identifying label shifts in target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA methods demonstrate that RAINCOAT can achieve an improvement in performance of up to 16.33%, and can effectively handle both closed-set and universal adaptation.

翻译：无监督域自适应（UDA）能够将源域中带标签数据集训练的模型迁移至无标签的目标域。然而，在处理复杂时间序列模型时，由于域间动态时间结构存在差异，导致时间与频率表征出现特征偏移和缺口，使得可迁移性面临挑战。此外，源域与目标域的任务可能具有截然不同的标签分布，这使得UDA难以缓解标签偏移并识别仅存在于目标域中的标签。我们提出RAINCOAT，这是首个面向复杂时间序列的闭集与通用域自适应统一模型。RAINCOAT通过联合考虑时域与频域特征、跨域对齐这些特征并修正错位以促进私有标签检测，从而解决特征偏移与标签偏移问题。同时，RAINCOAT通过识别目标域的标签偏移来提升可迁移性。我们在5个数据集上与13种最先进UDA方法进行的实验表明，RAINCOAT最高可实现16.33%的性能提升，并能有效处理闭集与通用两类自适应任务。