Time-Frequency Decoupled Cross-Scale Partial Optimal Transport for Time Series Domain Adaptation

Unsupervised domain adaptation has become an important paradigm for mitigating distribution shift in time series classification. However, existing time series domain adaptation methods typically align source and target data at a fixed time scale and simply fuse time-frequency features before uniform alignment. Furthermore, they do not consider the impact of noisy samples on distribution alignment. To address these limitations, we propose CSPAN (Time-Frequency Decoupled \textbf{C}ross-\textbf{S}cale \textbf{P}artial Optimal Transport \textbf{A}lignment \textbf{N}etwork) for time series domain adaptation. CSPAN first constructs segment-scale time-frequency candidates, and then uses Segment-Scale Aware Top-k Fusion to select the most suitable segment-scale feature representation. It then performs cross-domain alignment after decoupling time and frequency through Temporal Partial Optimal Transport and Frequency Partial Optimal Transport, where temporal domain alignment is guided by the transferability estimated from a probing transport plan, and frequency domain alignment is guided by the discriminability estimated by an auxiliary frequency classifier. By combining segment-scale representation selection, time-frequency decoupling, and partial distribution alignment, CSPAN enables more flexible cross-domain matching. Extensive experiments on six datasets demonstrate CSPAN’s consistent superiority, achieving an average accuracy improvement of 3.16\% in cross-domain scenarios. Code is available at the anonymous link: \url{https://anonymous.4open.science/r/CSPAN-35FB/}.