Unsupervised Domain Adaptation (UDA) methods have been successful in reducing
label dependency by minimizing the domain discrepancy between a labeled source
domain and an unlabeled target domain. However, these methods face challenges
when dealing with Multivariate Time-Series (MTS) data. MTS data typically
consist of multiple sensors, each with its own unique distribution. This
characteristic makes it hard to adapt existing UDA methods, which mainly focus
on aligning global features while overlooking the distribution discrepancies at
the sensor level, to reduce domain discrepancies for MTS data. To address this
issue, a practical domain adaptation scenario is formulated as Multivariate
Time-Series Unsupervised Domain Adaptation (MTS-UDA). In this paper, we propose
SEnsor Alignment (SEA) for MTS-UDA, aiming to reduce domain discrepancy at both
the local and global sensor levels. At the local sensor level, we design
endo-feature alignment, which aligns sensor features and their correlations
across domains. To reduce domain discrepancy at the global sensor level, we
design exo-feature alignment that enforces restrictions on global sensor
features. We further extend SEA to SEA++ by enhancing the endo-feature
alignment. Particularly, we incorporate multi-graph-based high-order alignment
for both sensor features and their correlations. Extensive empirical results
have demonstrated the state-of-the-art performance of our SEA and SEA++ on
public MTS datasets for MTS-UDA.

SEA and SEA++ are proposed for Multivariate Time-Series Unsupervised Domain Adaptation (MTS-UDA), aiming to reduce domain discrepancies at both the local and global sensor levels, and have demonstrated state-of-the-art performance on public MTS datasets for MTS-UDA.