Multi-Level Transport Alignment for Time-Series Domain Generalization

Time-series domain generalization is difficult because class semantics are often entangled with domain-specific signal variation: the same class may appear differently across subjects, sensors, datasets, and recording conditions. Existing pairwise or contrastive alignment methods typically define relations locally, either by matching selected sample pairs or by normalizing relations separately for each sample, which can miss the global structure induced by class semantics and class-conditional domain variation. We propose Multi-Level Transport Alignment (**Multa**), a balanced optimal transport objective for time-series domain generalization. **Multa** learns a globally organized transport structure in the unit representation space, encouraging class-discriminative representations while suppressing spurious source-domain shortcuts. We further introduce an adaptive alignment-gap reweighting mechanism that focuses optimization on samples whose learned alignment structure deviates most from the target relation. We show that globally balanced optimal transport yields a tighter bound on class-conditional source variation than row-wise local matching for fixed representations, and derive a generalization bound linking Multa’s transport objective to unseen-domain risk. Across five leave-one-domain-out cross-dataset sleep staging tasks and 25 cross-person human activity recognition tasks, **Multa** consistently improves over existing baselines under both dataset-level and subject-level domain shifts.