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

2024-11-19

arXiv.org

AI Summary

Addressing the challenges of modeling long-range dependencies and inefficient dynamic similarity measurement in multivariate time series (MTS) node classification, this paper proposes a contrastive similarity-aware dual-path Mamba architecture. Methodologically, it introduces the first integration of FastDTW to construct dynamic similarity matrices and incorporates a Kolmogorov–Arnold-enhanced graph isomorphism network (KA-GIN) to jointly optimize temporal dependency modeling and structural similarity learning. Additionally, a temporal contrastive learning mechanism is designed to enhance discriminative representation learning. Evaluated on the UEA multivariate time series benchmark, the method achieves significant improvements over state-of-the-art approaches under both supervised and semi-supervised settings, with consistent gains in node classification accuracy. These results validate its effectiveness and generalizability in capturing complex high-dimensional temporal dependencies and dynamic relational structures.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Model long-range dependencies in multivariate time series data
• Effectively capture dynamic similarities in high-dimensional MTS
• Improve node classification accuracy for diverse MTS applications

Innovation

• Temporal contrast learning for MTS representations
• FastDTW for dynamic similarity matrix construction
• DPMamba captures bidirectional MTS dependencies