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

2025-07-06

AI Summary

Existing multi-scale time series forecasting methods struggle to eliminate redundant shared features across scales, leading to imbalanced modeling of shared versus scale-specific features. To address this, we propose DisMS-TS—a novel framework that, for the first time, explicitly disentangles temporal representations into scale-shared and scale-specific components. DisMS-TS introduces a learnable temporal disentanglement module, coupled with two complementary regularizers: a consistency regularizer enforcing cross-scale invariance of shared features, and a discriminability regularizer enhancing the separability of scale-specific features. This end-to-end deep architecture jointly integrates multi-scale analysis and regularized feature disentanglement. Extensive experiments on multiple standard benchmarks demonstrate substantial improvements in time series classification performance, with up to a 9.71% absolute accuracy gain. The results validate both the effectiveness and generalizability of explicit disentangled representation learning for multi-scale time series classification.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Computer Vision: Representation Learning for Vision
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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%.

Problem

• Eliminating redundant shared features in multi-scale time series
• Improving time series classification prediction performance
• Disentangling scale-shared and scale-specific temporal representations

Innovation

• Eliminates redundant shared multi-scale features
• Uses temporal disentanglement for scale representations
• Applies regularization for consistent and disparate learning