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 domain-invariant semantics in multivariate time series, so that these preserve label information for the prediction task. In our framework, we further capture semantic variation between source and target domain via nearest-neighbor contrastive learning. To the best of our knowledge, ours is the first framework to learn domain-invariant semantic information for UDA of time series data. We evaluate our framework using large-scale, real-world datasets with medical time series (i.e., MIMIC-IV and AmsterdamUMCdb) to demonstrate its effectiveness and show that it achieves state-of-the-art performance for time series UDA.

本文提出了一种名为CLUDA的新颖的时序数据无监督领域适应框架，旨在使用对比学习框架来学习多变量时间序列的语境表示，并通过自定义最近邻对比学习来捕获源域和目标域之间的上下文表示变化，从而实现了在时间序列领域中学习域不变上下文表示的方法，并可用于对多种时间序列数据集进行评估以证明其有效性和卓越性能。

无监督域自适应时间序列的对比学习