  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.
