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MST-GNN: A Multi-scale Temporal-Enhanced Graph Neural Network for Anomaly Detection in Multivariate Time Series

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Web and Big Data (APWeb-WAIM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13421))

Abstract

Anomaly detection in time is an important task in many applications. Sensors are deployed in the industrial site to monitor the condition of different attributes or different places in real time, which generate multivariate time series. Recently, many methods were proposed to detect anomalies with multivariate time series, but they focused on the sequence attributes or spatial and temporal correlation, ignoring the characteristic of single sensor time series. In this paper, we propose a novel model MST-GNN that builds each sensor representation from Multi-Scale Temporal (MST) view, and use Graph Neural Network to mine their latent correlation to improve the performance of anomaly detection. In the MST representation, shapelets learning is introduced to extract its distinguishing features, a recurrent-skip neural network is used to extract the local temporal dependence relationship, and the raw data retains the original features of time series. These three features are fused to form the multi-scale temporal-enhanced features. Subsequently, the graph neural network is adopted to capture the potential interdependencies between multivariate time series and obtain the optimal representation of time series. Finally, bias assessment and anomaly detection are carried out. Extensive experiments on real-world datasets show that MST-GNN outperforms other state-of-the-art methods consistently, which provides an effective solution for anomaly detection in multivariate time series.

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References

  1. Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2), 1–38 (2021)

    Article  Google Scholar 

  2. Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. Association for Computing Machinery, Paris (2009)

    Google Scholar 

  3. Deng, A., Hooi, B.: Graph neural network-based anomaly detection in multivariate time series. In: AAAI Conference on Artificial Intelligence. 35(5), 4027–4035. AAAI Press, New York (2021)

    Google Scholar 

  4. Li, S., Wen, J.: A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform. Energy and Buildings. 68, 63–71 (2014)

    Article  Google Scholar 

  5. Ying, S., Wang, B., Wang, L., et al.: An improved KNN-based efficient log anomaly detection method with automatically labeled samples. ACM Trans. Knowl. Discov. Data 15(3), 1–22 (2021)

    Article  Google Scholar 

  6. Kriegel, H.P., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 444–452. Association for Computing Machinery, New York (2008)

    Google Scholar 

  7. Cheng, Z., Yang, Y., Wang, W., Zhuang, Y., Song, G.: Time2graph: revisiting time series modeling with dynamic shapelets. In: AAAI Conference on Artificial Intelligence. 34(04), 3617–3624. AAAI Press, New York (2020)

    Google Scholar 

  8. Ito, T., Tsubouchi, K., Sakaji, H., Yamashita, T., Izumi, K.: Contextual sentiment neural network for document sentiment analysis. Data Science and Engineering 5(2), 180–192 (2020)

    Article  MATH  Google Scholar 

  9. Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 392–401. ACM Press, New York (2014)

    Google Scholar 

  10. Lai, G., Chang, W. C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95–104. ACM Press, Michigan (2018)

    Google Scholar 

  11. Park, D., Hoshi, Y., Kemp, C.C.: A multimodal anomaly detector for robot-assisted feeding using an lstm-based variational autoencoder. IEEE Robotics and Automation Letters. 3(3), 1544–1551 (2018)

    Article  Google Scholar 

  12. Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., Kůrková, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703–716. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30490-4_56

    Chapter  Google Scholar 

  13. Zhao, H., Wang, Y., Duan, J., et al.: Multivariate time-series anomaly detection via graph attention network. In: IEEE ICDM, pp. 841–850. IEEE Press, Sorrento (2020)

    Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (No. 61872260) and National key research and development program of China (No. 2021YFB3300503).

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Correspondence to Li Wang .

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Ning, Z., Jiang, Z., Miao, H., Wang, L. (2023). MST-GNN: A Multi-scale Temporal-Enhanced Graph Neural Network for Anomaly Detection in Multivariate Time Series. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-25158-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25157-3

  • Online ISBN: 978-3-031-25158-0

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