  Unsupervised Domain Adaptation (UDA) aims to harness labeled source data to
train models for unlabeled target data. Despite extensive research in domains
like computer vision and natural language processing, UDA remains underexplored
for time series data, which has widespread real-world applications ranging from
medicine and manufacturing to earth observation and human activity recognition.
Our paper addresses this gap by introducing a comprehensive benchmark for
evaluating UDA techniques for time series classification, with a focus on deep
learning methods. We provide seven new benchmark datasets covering various
domain shifts and temporal dynamics, facilitating fair and standardized UDA
method assessments with state of the art neural network backbones (e.g.
Inception) for time series data. This benchmark offers insights into the
strengths and limitations of the evaluated approaches while preserving the
unsupervised nature of domain adaptation, making it directly applicable to
practical problems. Our paper serves as a vital resource for researchers and
practitioners, advancing domain adaptation solutions for time series data and
fostering innovation in this critical field. The implementation code of this
benchmark is available at https://github.com/EricssonResearch/UDA-4-TSC.
