Unsupervised domain adaptation (UDA) aims at learning a machine learning model using a labeled source domain that performs well on a similar yet different, unlabeled target domain. UDA is important in many applications such as medicine, where it is used to adapt risk scores across different patient cohorts. In this paper, we develop a novel framework for UDA of time series data, called CLUDA. Specifically, we propose a contrastive learning framework to learn contextual representations in multivariate time series, so that these preserve label information for the prediction task. In our framework, we further capture the variation in the contextual representations between source and target domain via a custom nearest-neighbor contrastive learning. To the best of our knowledge, ours is the first framework to learn domain-invariant, contextual representation for UDA of time series data. We evaluate our framework using a wide range of time series datasets to demonstrate its effectiveness and show that it achieves state-of-the-art performance for time series UDA.

翻译：无监督域适应旨在利用带标签的源域学习机器学习模型，使其在相似但不同的无标签目标域上表现良好。该技术在医学等众多应用中至关重要——例如用于调整不同患者群体的风险评分。本文提出了一种名为CLUDA的时间序列数据无监督域适应新框架。具体而言，我们构建了一个对比学习框架来学习多元时间序列中的上下文表征，使这些表征能够保留预测任务所需的标签信息。在该框架中，我们进一步通过自定义的最近邻对比学习捕捉源域与目标域之间上下文表征的变异。据我们所知，这是首个为时间序列无监督域适应学习域不变性上下文表征的框架。我们通过多种时间序列数据集评估该框架，验证其有效性并证明其在时间序列无监督域适应任务中达到了最优性能。