Learning Style-Invariant Temporal Semantics for Source-Free Time-Series Domain Adaptation

We study source-free time-series domain adaptation (SFTSDA), where a source-trained predictor is transferred to an unlabeled target domain while source samples are unavailable. Existing approaches improve transfer through imputation, time–frequency modeling, and target self-training, yet target-specific temporal style may remain entangled with class-relevant semantics, causing style variation to be interpreted as class evidence and subsequently reinforced through pseudo-label adaptation. We propose style-invariant temporal semantics (SITS), which formulates semantic invariance through target-style orbit learning. Target-only channel and spectral statistics define closed-form style transformations, while evolving temporal semantic neighborhoods determine which transformations are admissible for each sample, forming a target-style orbit. SITS then learns relation-aware temporal semantics by maximizing the worst-case class margin over each orbit. Progressive pseudo-posterior refinement updates the semantic neighborhoods used to construct the next orbit. We further show that a positive orbit margin preserves the predicted class over the covered target-style family, providing a direct link between the orbit-wise objective and style-invariant temporal semantics. Experiments on three real-world time-series datasets evaluate the proposed SITS framework against representative source-free and test-time adaptation baselines. The code is available at https://github.com/Sky-byte-box/SITS_code.