Publication
Which Time Series Domain Shifts can Neural Networks Adapt to?
Henri Hoyez; Bruno Mirbach; Jason Raphael Rambach; Cedric Schockaert; Didier Stricker
In: Proceedings of. European Signal Processing Conference (EUSIPCO-2024), IEEE, 2024.
Abstract
Machine Learning on time series has great potential
in solving real-life problems, from medical monitoring to machine
predictive maintenance. In practice, however, the deployment
domain can differ from the training conditions. These domain
shifts give rise to the domain adaptation field which extends
to time series data. However, despite the fact that broad domain
shift definitions were already proposed, there is no clear definition
adapted specifically to time series in the current literature. Moreover, even though evaluation methods were proposed, evaluations
on real data do not allow a full control over the benchmarks.
In this paper, we first propose a novel definition of domain shift
based on a State Space Model (SSM). Then, we introduce a
new dataset based on these SSMs to provide a benchmark using
controlled domain shifts. Lastly, we perform an evaluation of the
State of the Art on these generated domain adaptation problems,
and hence systematically evaluate the domain shift effects on the
adaptation performances.