MSNet and LS-Net: Scalable Multi-Scale Multi-Representation Networks for Time Series Classification

ArXi:2603.19315v1 Announce Type: new Time series classification (TSC) performance depends not only on architectural design but also on the diversity of input representations. In this work, we propose a scalable multi-scale convolutional framework that systematically integrates structured multi-representation inputs for univariate time series. We evaluate all models across 142 benchmark datasets under a unified experimental protocol. Critical Difference analysis confirms statistically significant performance differences among the top models.