  Unsupervised domain adaptation (UDA) has successfully addressed the domain
shift problem for visual applications. Yet, these approaches may have limited
performance for time series data due to the following reasons. First, they
mainly rely on large-scale dataset (i.e., ImageNet) for the source pretraining,
which is not applicable for time-series data. Second, they ignore the temporal
dimension on the feature space of the source and target domains during the
domain alignment step. Last, most of prior UDA methods can only align the
global features without considering the fine-grained class distribution of the
target domain. To address these limitations, we propose a Self-supervised
Autoregressive Domain Adaptation (SLARDA) framework. In particular, we first
design a self-supervised learning module that utilizes forecasting as an
auxiliary task to improve the transferability of the source features. Second,
we propose a novel autoregressive domain adaptation technique that incorporates
temporal dependency of both source and target features during domain alignment.
Finally, we develop an ensemble teacher model to align the class-wise
distribution in the target domain via a confident pseudo labeling approach.
Extensive experiments have been conducted on three real-world time series
applications with 30 cross-domain scenarios. Results demonstrate that our
proposed SLARDA method significantly outperforms the state-of-the-art
approaches for time series domain adaptation.

Machine Learning