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

TL;DR

DisMS-TS introduces a novel framework that disentangles and eliminates redundant shared features across multiple temporal scales in time series data, significantly enhancing classification accuracy.

Contribution

The paper presents a new end-to-end disentangled multi-scale framework that effectively removes redundant features, improving time series classification performance.

Findings

Achieves up to 9.71% accuracy improvement over baselines

Effectively captures scale-specific and shared temporal features

Validated on multiple datasets

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%.