MHCCL: Masked Hierarchical Cluster-wise Contrastive Learning for Multivariate Time Series

Meng, Qianwen; Qian, Hangwei; Liu, Yong; Xu, Yong; Shen, Zhiqi; Cui, Lizhen

doi:10.48550/arxiv.2212.01141

Cited by 1 publication

(2 citation statements)

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“…To evaluate performance, we adopt two metrics, i.e., Accuracy (Accu.) and Macro-averaged F1-Score (MF1) (Eldele et al 2021;Meng et al 2022). Besides, to reduce the effect of random initialization, we conduct ten times for all experiments and take the average results for comparisons.…”

Section: Resultsmentioning

confidence: 99%

“…We compare our method with SOTA methods, including TNC (Tonekaboni, Eytan, and Goldenberg 2021), TS-TCC (Eldele et al 2021), TS2Vec (Yue et al 2022), MHCCL (Meng et al 2022), CaSS (Chen et al 2022), and TAGCN (Zhang et al 2023d). All methods are reimplemented based on their original settings except for the encoders, which are replaced by the same encoder as ours for fair comparisons.…”

Section: Comparisons With State-of-the-artsmentioning

confidence: 99%

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Graph-Aware Contrasting for Multivariate Time-Series Classification

Wang,

Xu,

Yang

et al. 2024

AAAI

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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.

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Section: Resultsmentioning

confidence: 99%