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HAN-CAD: hierarchical attention network for context anomaly detection in multivariate time series

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Abstract

Anomaly Detection in multivariate time series (MTS) plays an important role in many real-world Web services such as the Web traffic monitoring system. With abundant MTS data, exploiting the relationships among different variables, i.e., inter-variable relationships, is crucial for detecting anomalies. Recent studies have made substantial efforts to promote relationship learning from graph neural network. However, existing methods mostly neglect the distinctive characteristics of inter-variable relationships under different contexts, i.e., dynamics of inter-variable relationships. Therefore, we propose a “Hierarchical Attention Networks for Context Anomaly Detection” (HAN-CAD) model to fully exploit the inter-variable relationships and their dynamics. More concretely, we model each time series segment (context sequence) as a graph, where variables in the sequence are nodes and edges denote correlation patterns among variables. Then, the first graph attention layer is built on this graph to obtain the variable representation, which captures the relationships among different variables. Thereafter, the second attention layer outputs the sequence representation by integrating inter-variable relationships within the current context sequence. Finally, anomalies can be detected based on the reconstruction model, i.e., AutoEncoder. Extensive experiments on real-world datasets demonstrate that the proposed method can effectively detect anomalies in MTS and outperforms recent state-of-the-art methods.

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Data availability

The “ASD” dataset that supports the findings of this study is publicly available in “https://github.com/zhhlee/InterFusion”. And the “SMD” and “’WADI” datasets are available “https://itrust.sutd.edu.sg/itrust-labs_datasets/”.

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Funding

This research is partially supported by the Key Program of National Natural Science Foundation of China under grant 92046026, in part by the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province under grant 21KJB520034.

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Authors and Affiliations

  1. College of Information Engineering, Nanjing University of Finance & Economic, 3 Wenyuan Road, Nanjing, 210023, Jiangsu, China

    Haicheng Tao & Jiawei Miao

  2. Jiangsu Electric Power Information Technology Company, 22 West Beijing Road, Nanjing, 210000, Jiangsu, China

    Lin Zhao, Zhenyu Zhang & Shuming Feng

  3. State Grid Jiangsu Electric Power Company, Changzhou, 27 Juqian Street, Changzhou, 213000, Jiangsu, China

    Shu Wang

  4. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, 230009, Anhui, China

    Jie Cao

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  1. Haicheng Tao
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  2. Jiawei Miao
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  3. Lin Zhao
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  4. Zhenyu Zhang
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  5. Shuming Feng
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  6. Shu Wang
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  7. Jie Cao
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Contributions

Haicheng Tao and Jie cao designed the model, drafted the work and wrote the main manuscript text. Jiawei Miao, Haicheng Tao, Lin Zhao and Zhenyu Zhang analysed the data and carried out the experiment. Shuming Feng and Shu Wang contributed to the interpretation of the results. All authors provided critical feedback and helped shape the research, analysis and manuscript.

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Correspondence to Jie Cao.

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Tao, H., Miao, J., Zhao, L. et al. HAN-CAD: hierarchical attention network for context anomaly detection in multivariate time series. World Wide Web 26, 2785–2800 (2023). https://doi.org/10.1007/s11280-023-01171-1

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  • DOI: https://doi.org/10.1007/s11280-023-01171-1

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