Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings

arXiv:2403.09749v1 Announce Type: new
Abstract: In Time Series Classification (TSC), temporal pooling methods that consider sequential information have been proposed. However, we found that each temporal pooling has a distinct mechanism, and can perform better or worse depending on time series data. We term this fixed pooling mechanism a single perspective of temporal poolings. In this paper, we propose a novel temporal pooling method with diverse perspective learning: Selection over Multiple Temporal Poolings (SoM-TP). SoM-TP dynamically selects the optimal temporal …

abstract arxiv classification cs.ai cs.lg data diverse found however information multiple perspective pooling series temporal time series type