Skip to main content

Online Multivariate Time Series Anomaly Detection Method Based on Contrastive Learning

  • Conference paper
  • First Online:
Advanced Intelligent Computing Technology and Applications (ICIC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14874))

Included in the following conference series:

  • 503 Accesses

Abstract

With the swift progression of industrial automation and Internet of Things technologies, the importance of multivariate time series anomaly detection has markedly increased, serving as a vital tool for identifying abnormal behaviors within complex datasets to prevent potential risks. Traditional anomaly detection methods often struggle to deal with multivariable and unlabeled data environments, especially in the context of real-time dynamic data streams, where traditional models require frequent retraining to adapt to new anomaly patterns. To address this challenge, our work proposes an online anomaly detection model for multivariate time series based on a contrastive learning framework:ODAnomaly, utilizing a dual autocorrelation mechanism to effectively extract features of normal data and distinguish anomalous data. The model features an online learner that uses gradient updates and Pearson correlation coefficients to rapidly adapt to new anomaly patterns, boosting its real-time learning efficiency. A contrastive loss function, informed by homoscedastic uncertainty, aids in anomaly detection through data representation. This approach reduces reliance on extensively labeled data and enhances the model’s adaptability and accuracy in real-time data streams. It provides an efficient and cost-effective solution for advancing multivariate time series anomaly detection in both research and practical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Castellani, A., Schmitt, S., Squartini, S.: Real-world anomaly detection by using digital twin systems and weakly supervised learning. IEEE Trans. Industr. Inf. 17(7), 4733–4742 (2020)

    Article  Google Scholar 

  2. Zavrtanik, V., Kristan, M., Skočaj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021)

    Article  Google Scholar 

  3. Jin, M., Liu, Y., Zheng, Y., et al.: Anemone: Graph anomaly detection with multi-scale contrastive learning. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3122–3126 (2021)

    Google Scholar 

  4. Wang, X., Pi, D., Zhang, X., et al.: Variational transformer-based anomaly detection approach for multivariate time series. Measurement 191, 110791 (2022)

    Article  Google Scholar 

  5. Gu, M., Fei, J., Sun, S.: Online anomaly detection with sparse Gaussian processes. Neurocomputing 403, 383–399 (2020)

    Article  Google Scholar 

  6. Pang, G., Shen, C., Cao, L., et al.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54(2), 1–38 (2021)

    Article  Google Scholar 

  7. Jiang, J.R., Kao, J.B., Li, Y.L.: Semi-supervised time series anomaly detection based on statistics and deep learning. Appl. Sci. 11(15), 6698 (2021)

    Article  Google Scholar 

  8. Saqib, M., Şentürk, E., Sahu, S.A., et al.: Ionospheric anomalies detection using autoregressive integrated moving average (ARIMA) model as an earthquake precursor. Acta Geophys. 69(4), 1493–1507 (2021)

    Article  Google Scholar 

  9. Kozitsin, V., Katser, I., Lakontsev, D.: Online forecasting and anomaly detection based on the ARIMA model. Appl. Sci. 11(7), 3194 (2021)

    Article  Google Scholar 

  10. Jain, M., Kaur, G., Saxena, V.: A K-Means clustering and SVM based hybrid concept drift detection technique for network anomaly detection. Expert Syst. Appl. 193, 116510 (2022)

    Article  Google Scholar 

  11. Hosseinzadeh, M., Rahmani, A.M., Vo, B., et al.: Improving security using SVM-based anomaly detection: issues and challenges. Soft. Comput. 25(4), 3195–3223 (2021)

    Article  Google Scholar 

  12. Beggel, L., Pfeiffer, M., Bischl, B.: Robust anomaly detection in images using adversarial autoencoders. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11906, pp. 206–222. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-46150-8_13

    Chapter  Google Scholar 

  13. Ullah, I., Mahmoud, Q.H.: Design and development of RNN anomaly detection model for IoT networks. IEEE Access 10, 62722–62750 (2022)

    Article  Google Scholar 

  14. Bergmann, P., Batzner, K., Fauser, M., et al.: The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. Int. J. Comput. Vision 129(4), 1038–1059 (2021)

    Article  Google Scholar 

  15. Jiang, T., Li, Y., Xie, W., et al.: Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection. IEEE Trans. Geosci. Remote Sens. 58(7), 4666–4679 (2020)

    Article  Google Scholar 

  16. Zhang, H., Jiang, H, Lu, Y., et al.: Research on an abnormal recognition method of the UHV reactor based on the DAGMM. In: J. Phys. Conf. Ser. 2532(1), 012012 (2023)

    Google Scholar 

  17. Shin, Y., Lee, S., Tariq, S., et al.: ITAD: integrative tensor-based anomaly detection system for reducing false positives of satellite systems. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 2733–2740 (2020)

    Google Scholar 

  18. Wu, H., Xu, J., Wang, J., et al.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv. Neural. Inf. Process. Syst. 34, 22419–22430 (2021)

    Google Scholar 

  19. Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  20. Aytekin, C., Ni, X., Cricri, F., et al.: Clustering and unsupervised anomaly detection with l 2 normalized deep auto-encoder representations. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 IEEE (2018)

    Google Scholar 

  21. Grill, J.B., Strub, F., Altché, F., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271–21284 (2020)

    Google Scholar 

  22. Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)

    Google Scholar 

  23. Defard, T., Setkov, A., Loesch, A., Audigier, R.: PaDiM: a patch distribution modeling framework for anomaly detection and localization. In: Del Bimbo, A., Cucchiara, R., Sclaroff, S., Farinella, G.M., Mei, T., Bertini, M., Escalante, H.J., Vezzani, R. (eds.) ICPR 2021. LNCS, vol. 12664, pp. 475–489. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68799-1_35

    Chapter  Google Scholar 

  24. Su, Y., Zhao, Y., Niu, C., et al.: Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2828–2837 (2019)

    Google Scholar 

  25. Abdulaal, A., Liu, Z., Lancewicki, T.: Practical approach to asynchronous multivariate time series anomaly detection and localization. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2485–2494 (2021)

    Google Scholar 

  26. Hundman, K., Constantinou, V., Laporte, C., et al.: Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 387–395 (2018)

    Google Scholar 

  27. Mathur, A.P., Tippenhauer, N.O.: SWaT: a water treatment testbed for research and training on ICS security. In: 2016 International Workshop on Cyber-Physical Systems for Smart Water Networks (CySWater), pp. 31–36. IEEE (2016)

    Google Scholar 

  28. Stenholm, A.R., Dalsgaard, I., Middelboe, M.: Isolation and characterization of bacteriophages infecting the fish pathogen Flavobacterium psychrophilum. Appl. Environ. Microbiol. 74(13), 4070–4078 (2008)

    Article  Google Scholar 

  29. Anderson, O.D.: Time-Series, 2nd edn. (1976)

    Google Scholar 

  30. Yang, Y., Zhang, C., Zhou, T., et al.: Dcdetector: Dual attention contrastive representation learning for time series anomaly detection. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3033–3045 (2023)

    Google Scholar 

Download references

Acknowledgments

This work is partially supported by a grant from the National Natural Science Foundation of China (No. 62032017, No. 62272368), Key Talent Project of Xidian University (No. QTZX24004), the Innovation Capability Support Program of Shaanxi (No. 2023-CX-TD-08), Shaanxi Qinchuangyuan “scientists+engineers” team (No.2023KXJ-040), Science and Technology Program of Xi’an(No.23KGDW0005-2022).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Hui Liu or Junzhao Du .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dong, X., Liu, H., Du, J., Wang, Z., Wang, C. (2024). Online Multivariate Time Series Anomaly Detection Method Based on Contrastive Learning. In: Huang, DS., Chen, W., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14874. Springer, Singapore. https://doi.org/10.1007/978-981-97-5618-6_39

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-5618-6_39

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-5617-9

  • Online ISBN: 978-981-97-5618-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics