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Hybrid graph transformer networks for multivariate time series anomaly detection

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Abstract

Anomaly detection for multivariate time series has been widely used in industry and has become one of the hot research problems in the field of data mining. However, the existing anomaly detection methods still have the following limitations: (1) The topological relationship between sensors is complex and nonlinear, and it is difficult to effectively model the inter-sensor dependencies. (2) Most anomaly detection models tend to ignore the critical temporal information in different time steps when capturing the temporal dependencies. To address these problems, we propose a hybrid graph transformer network for multivariate time series anomaly detection (HGTMAD), which combines the transformer with graph convolution to predict multivariate time series-based anomalies. In the time domain, we design a new sparse metric transformer network to capture the time dependence, where the Wasserstein distance is used to measure out significant dot product pairs to learn a better time series representation. In the spatial domain, we design a new channel convolution transformer network fused with a graph convolution network to learn accurately the complex dependencies of multivariate time series in spatial and on feature dimensions based on a combination of local and global approaches. Extensive experiments on publicly available datasets further demonstrate that the proposed HGTMAD significantly outperforms the mainstream state-of-the-art methods.

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Data availability

All of our datasets come from public datasets. You can go to the corresponding official website to download.

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Funding

This work described in this paper was supported by the Open Foundation of State Key Laboratory for Novel Software Technology at Nanjing University of 775 P.R.China (No. KFKT2021B12). This work is supported in part by the Future Network Scientific Research Fund Project (FNSRFP-2021-YB-54), the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (17KJB520028) and Tongda College of Nanjing University of Posts and Telecommunications (XK203XZ21001).

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Authors and Affiliations

  1. School of Computer Science, Hubei University of Technology, Wuhan, 430068, China

    Rong Gao, Wei He, Lingyu Yan & Zhiwei Ye

  2. State Key Laboratory for Novel Software Technology at Nanjing University, Nanjing University, Nanjing, 210023, China

    Rong Gao

  3. Information Center, China Waterborne Transport Research Institue, Beijing, 100080, China

    Donghua Liu

  4. College of Tongda, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Yonghong Yu

Authors
  1. Rong Gao
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  2. Wei He
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  3. Lingyu Yan
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  4. Donghua Liu
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  5. Yonghong Yu
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  6. Zhiwei Ye
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Contributions

RG: Conceptualization, Methodology, Software. WH: Data curation, Writing-Original draft preparation. LY: Supervision, Writing. DL: Supervision, Writing—review & editing. YY: Supervision, Writing—review & editing. ZY: Review, Editing.

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Correspondence to Lingyu Yan.

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Gao, R., He, W., Yan, L. et al. Hybrid graph transformer networks for multivariate time series anomaly detection. J Supercomput 80, 642–669 (2024). https://doi.org/10.1007/s11227-023-05503-w

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