DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification

• URL: http://arxiv.org/abs/2507.04600v2
• Date: Thu, 24 Jul 2025 09:29:08 GMT
• Title: DisMS-TS: Eliminating Redundant Multi-Scale Features for Time Series Classification
• Authors: Zhipeng Liu, Peibo Duan, Binwu Wang, Xuan Tang, Qi Chu, Changsheng Zhang, Yongsheng Huang, Bin Zhang,
• Abstract summary: We propose a novel end-to-end Disentangled Multi-Scale framework for Time Series classification (DisMS-TS)<n>DisMS-TS is designed to eliminate redundant shared features in multi-scale time series, thereby improving prediction performance.<n>Experiments conducted on multiple datasets validate the superiority of DisMS-TS over its competitive baselines, with the accuracy improvement up to 9.71%.
• Score: 14.947369878718822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world time series typically exhibit complex temporal variations, making the time series classification task notably challenging. Recent advancements have demonstrated the potential of multi-scale analysis approaches, which provide an effective solution for capturing these complex temporal patterns. However, existing multi-scale analysis-based time series prediction methods fail to eliminate redundant scale-shared features across multi-scale time series, resulting in the model over- or under-focusing on scale-shared features. To address this issue, we propose a novel end-to-end Disentangled Multi-Scale framework for Time Series classification (DisMS-TS). The core idea of DisMS-TS is to eliminate redundant shared features in multi-scale time series, thereby improving prediction performance. Specifically, we propose a temporal disentanglement module to capture scale-shared and scale-specific temporal representations, respectively. Subsequently, to effectively learn both scale-shared and scale-specific temporal representations, we introduce two regularization terms that ensure the consistency of scale-shared representations and the disparity of scale-specific representations across all temporal scales. Extensive experiments conducted on multiple datasets validate the superiority of DisMS-TS over its competitive baselines, with the accuracy improvement up to 9.71%.

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