TimeSWARM: Soft Weighted Assignment-Based Relational Modeling of Channel-Patches for Multivariate Time Series Classification

Multivariate time series classification (MTSC) requires jointly modeling discriminative temporal patterns and dependencies across variables or channels. Existing methods typically process channels independently, represent interactions only at the channel level, or route an entire multivariate patch as a single unit, thereby overlooking heterogeneous local dynamics across channels and temporal regions. We address this limitation by coupling local representation specialization with cross-channel relational learning at the channel-patch level. To this end, we propose TimeSWARM, a mixture-of-experts framework for dynamic channel-patch specialization and relational modeling. Each channel is partitioned into local patches, and an input-conditioned router combines temporal and spectral descriptors to produce expert assignments for each channel-patch. Sparsified routing weights are used to aggregate multi-scale temporal experts, enabling specialization across channels, temporal regions, and samples. The resulting dense routing distributions are reused as soft latent-group assignments for cross-channel interaction, so that the same routing mechanism determines both how each local pattern is represented and which channels it communicates with. Extensive experiments on the UEA multivariate time series archive show that TimeSWARM achieves the best average accuracy and average rank among the compared methods, together with the highest number of dataset-level top-1 results.