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A contrastive autoencoder with multi-resolution segment-consistency discrimination for multivariate time series anomaly detection

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Abstract

Most reconstruction-based multivariate time series (MTS) anomaly detection methods tend to learn point-wise information, failing to extract a robust overall representation. Some studies have tried to introduce contrastive learning to alleviate this problem, but two key challenges remain: (1) Most data augmentation approaches follow the inductive bias from computer vision, which may destroy the time series patterns. (2) The instance discrimination proxy task of traditional contrastive learning will cause the generation of numerous false negative samples and the loss of common information when applied to MTS anomaly detection. In this paper, a contrastive autoencoder with multi-resolution segment-consistency discrimination (MRSCD) is proposed for MTS anomaly detection. Firstly, a time series data augmentation method is proposed, which constructs positive and negative samples by down-sampling the original time series in different resolutions and orders. Then an encoder is used to extract different levels of temporal information from the multi-resolution sample pairs which are randomly combined by positive and negative samples with the same resolution. Finally, a proxy task of segment-consistency discrimination is proposed for contrastive autoencoder to distinguish positive and negative sample pairs through the order of the down-sampled segments, enabling the model to extract inter-segment contextual information at the same time as learning point-wise information. The experimental results on five public datasets show that MRSCD significantly outperforms the baseline methods in three evaluation metrics.

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Data Availability

The datasets supporting the results of this article are SMD, SWaT, MSL, PSM, and SMAP public datasets, and the authors confirm that the datasets are indicated in the reference list.

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Acknowledgements

The authors would like to thank their colleagues from the machine learning group for discussions on this paper. This work was supported by the Science & Technology Project of State Grid Corporation of China (No.5400-202355230A-1-1-ZN)

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Authors and Affiliations

  1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Bing Xue, Xin Gao, Jiahao Yu, Shiyuan Fu, Lingli Chen & Zhihang Meng

  2. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Feng Zhai

  3. China Electric Power Research Institute Company Limited, Beijing, 100192, China

    Feng Zhai & Baofeng Li

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  1. Bing Xue
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  2. Xin Gao
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  3. Feng Zhai
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Contributions

Bing Xue: Methodology, Software, Writing - Original Draft, Writing - Review & Editing. Xin Gao: Conceptualization, Methodology, Supervision, Writing - Original Draft, Writing - Review & Editing. Feng Zhai: Conceptualization, Resources, Funding acquisition. Baofeng Li: Software, Validation, Funding acquisition. Jiahao Yu: Software, Validation. Shiyuan Fu: Software, Writing - Review & Editing. Lingli Chen: Writing - Review & Editing. Zhihang Meng: Writing - Review & Editing.

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Correspondence to Xin Gao.

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Xue, B., Gao, X., Zhai, F. et al. A contrastive autoencoder with multi-resolution segment-consistency discrimination for multivariate time series anomaly detection. Appl Intell 53, 28655–28674 (2023). https://doi.org/10.1007/s10489-023-04985-8

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