Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification

TL;DR

This paper introduces a novel dual-pathway model that leverages contrast similarity and dynamic time warping to effectively capture dependencies and similarities in multivariate time series data for improved node classification accuracy.

Contribution

The paper proposes CS-DPMamba, a new method combining contrast similarity, FastDTW, and graph neural networks to better model long-range dependencies and dynamic similarities in MTS data.

Findings

Outperforms existing methods on UEA MTS datasets

Effective in both supervised and semi-supervised settings

Accurately captures long-range and short-range dependencies

Abstract

Multivariate time series (MTS) data is generated through multiple sensors across various domains such as engineering application, health monitoring, and the internet of things, characterized by its temporal changes and high dimensional characteristics. Over the past few years, many studies have explored the long-range dependencies and similarities in MTS. However, long-range dependencies are difficult to model due to their temporal changes and high dimensionality makes it difficult to obtain similarities effectively and efficiently. Thus, to address these issues, we propose contrast similarity-aware dual-pathway Mamba for MTS node classification (CS-DPMamba). Firstly, to obtain the dynamic similarity of each sample, we initially use temporal contrast learning module to acquire MTS representations. And then we construct a similarity matrix between MTS representations using Fast Dynamic Time Warping (FastDTW). Secondly, we apply the DPMamba to consider the bidirectional nature of MTS, allowing us to better capture long-range and short-range dependencies within the data. Finally, we utilize the Kolmogorov-Arnold Network enhanced Graph Isomorphism Network to complete the information interaction in the matrix and MTS node classification task. By comprehensively considering the long-range dependencies and dynamic similarity features, we achieved precise MTS node classification. We conducted experiments on multiple University of East Anglia (UEA) MTS datasets, which encompass diverse application scenarios. Our results demonstrate the superiority of our method through both supervised and semi-supervised experiments on the MTS classification task.