META: Meta-learning Enhanced Temporal-Frequency Alignment for Time Series Domain Adaptation

Time series classification (TSC) underpins clinical decision support, yet differences in patient demographics and institutional data collection workflows induce domain shift that severely degrades model performance on the target domain. Unsupervised domain adaptation (UDA) mitigates this by aligning distributions, but we show that the two dominant alignment paradigms each leave a distinct structural blind spot. First, joint maximum mean discrepancy (JMMD) constrains only class-conditional means, leaving mean-preserving drifts in its null space unpenalized, which drifts enlarge intra-class dispersion, directly reducing discriminability. Second, contrastive learning pulls all same-class samples toward a single centroid, and this non-uniform attraction radially stretches each sub-mode, enlarging sub-modal dispersion and destroying the multi-modal structure that carries clinically meaningful heterogeneity. We address both blind spots with META, a unified time-series UDA framework comprising two modules: (i) Time-Frequency Contrastive Learning leverages the temporal dependency of time and the stability of frequency to learn complementary contextual representations. We prove that at its global optimum same-class features collapse to class centers forming a regular simplex. (ii) MetaMMD treats the Gaussian kernel bandwidths of MMD as learnable parameters optimized via bi-level optimization against a domain discrimination loss, which differentiates bandwidths so that same-sub-mode cross-domain kernel weights remain bounded below by a positive constant while cross-sub-mode weights vanish. We prove that this shrinks within-sub-mode variance exponentially and drives sub-modal dispersion toward zero without merging sub-modes. Experiments on eight datasets across four tasks demonstrate that META consistently outperforms state-of-the-art UDA and time-series UDA baselines.