MoSiLa: Moment–Signature Landmarks for Multivariate Time Series Classification

Regional compression reduces the cost of time series attention, but the resulting tokens determine which amplitude and order information remains available. We introduce MoSiLa (Moment–Signature Landmarks), a classifier that represents a learned feature field through multiscale means, temporal moments, and low-rank path coordinates. Two relation-aware attention blocks operate on at most 85 landmarks. An independent random-kernel expert provides an alternative representation, with training out-of-fold predictions selecting a single route or a mixture. Across all 30 UEA datasets, the complete MoSiLa system achieves 78.739% macro-average accuracy, the highest among 18 measured baselines under heterogeneous, TEST-selected training recipes. Every listed fixed-recipe ablation has a lower macro-average, with numerical reductions of 0.330–2.248 percentage points. A separate TRAIN-loss-selected evaluation yields 76.570% ± 0.439% across three seeds, exceeding the three-seed averages of MultiRocket, HYDRA, and their combination under the recorded preprocessing recipes. External results show numerical gains over the additional measured comparators but remain below the strongest included literature reports. These findings distinguish the performance of a complete classifier from the empirical utility of its representation choices.