Graph-Aware Contrasting for Multivariate Time-Series Classification

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Graph-Aware Contrasting for Multivariate Time-Series Classification
Title:
Graph-Aware Contrasting for Multivariate Time-Series Classification
Journal Title:
Proceedings of the AAAI Conference on Artificial Intelligence
Keywords:
Publication Date:
25 March 2024
Citation:
Wang, Y., Xu, Y., Yang, J., Wu, M., Li, X., Xie, L., & Chen, Z. (2024). Graph-Aware Contrasting for Multivariate Time-Series Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 38(14), 15725–15734. https://doi.org/10.1609/aaai.v38i14.29501
Abstract:
Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views of unlabeled samples and then learns effective representations for these samples. Existing contrastive learning methods mainly focus on achieving temporal consistency with temporal augmentation and contrasting techniques, aiming to preserve temporal patterns against perturbations for MTS data. However, they overlook spatial consistency that requires the stability of individual sensors and their correlations. As MTS data typically originate from multiple sensors, ensuring spatial consistency becomes essential for the overall performance of contrastive learning on MTS data. Thus, we propose Graph-Aware Contrasting for spatial consistency across MTS data. Specifically, we propose graph augmentations including node and edge augmentations to preserve the stability of sensors and their correlations, followed by graph contrasting with both node- and graph-level contrasting to extract robust sensor- and global-level features. We further introduce multi-window temporal contrasting to ensure temporal consistency in the data for each sensor. Extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on various MTS classification tasks. The code is available at https://github.com/Frank-Wang-oss/TS-GAC.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - AME Programmatic Funds
Grant Reference no. : A20H6b0151

This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Career Development Award
Grant Reference no. : C210112046

This research / project is supported by the National Research Foundation - AI Singapore Programme
Grant Reference no. : AISG2-RP-2021-027
Description:
ISSN:
2159-5399
2374-3468
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