  Unsupervised domain adaptation (UDA) enables the transfer of models trained
on source domains to unlabeled target domains. However, transferring complex
time series models presents challenges due to the dynamic temporal structure
variations across domains. This leads to feature shifts in the time and
frequency representations. Additionally, the label distributions of tasks in
the source and target domains can differ significantly, posing difficulties in
addressing label shifts and recognizing labels unique to the target domain.
Effectively transferring complex time series models remains a formidable
problem. We present Raincoat, the first model for both closed-set and universal
domain adaptation on complex time series. Raincoat addresses feature and label
shifts by considering both temporal and frequency features, aligning them
across domains, and correcting for misalignments to facilitate the detection of
private labels. Additionally, Raincoat improves transferability by identifying
label shifts in target domains. Our experiments with 5 datasets and 13
state-of-the-art UDA methods demonstrate that Raincoat can improve transfer
learning performance by up to 16.33% and can handle both closed-set and
universal domain adaptation.
