VGG Based Unsupervised Anomaly Detection in Multivariate Time Series

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1196)


Anomaly detection in time series data is a known problem, but recent growth in the number of units that can produce data require models that work on unlabelled and diverse types of data. We propose to adapt the neural network introduced by Simonyan and Zisserman in 2015 called VGG16 and used to detect and classify objects in images. We show that the VGG16 architecture with 2-dimensional convolutions replaced with 1-dimensional version could be a building block of an autoencoder approach to detect anomalies. Additionally we show that the proposed model achieves results that are similar or better than of classical anomaly detection methods.


Anomaly detection Time series Neural networks 


  1. 1.
    Aggarwal, C.C.: Outlier analysis. In: Data Mining. Springer, Cham (2015)Google Scholar
  2. 2.
    Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407 (2019)
  3. 3.
    Goodfellow, I., Bengio, Y.: Deep Learning. MIT Press, Cambridge (2016)zbMATHGoogle Scholar
  4. 4.
    Li, D., Chen, D., Goh, J., Ng, S.K.: Anomaly detection with generative adversarial networks for multivariate time series. arXiv preprint arXiv:1809.04758 (2018)
  5. 5.
    Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422. IEEE (2008)Google Scholar
  6. 6.
    Oh, D., Yun, I.: Residual error based anomaly detection using auto-encoder in SMD machine sound. Sensors 18(5), 1308 (2018)CrossRefGoogle Scholar
  7. 7.
    Ramaswamy, S., et al.: Efficient algorithms for mining outliers from large data sets. In: SIGMOD 2000: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, United States, pp. 427–438. ACM (2000)Google Scholar
  8. 8.
    Shipmon, D.T., Gurevitch, J.M., Piselli, P.M., Edwards, S.T.: Time series anomaly detection; detection of anomalous drops with limited features and sparse examples in noisy highly periodic data. arXiv preprint arXiv:1708.03665 (2017)
  9. 9.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
  10. 10.
    Zhao, Y., Nasrullah, Z., Li, Z.: PyOD: a Python toolbox for scalable outlier detection. J. Mach. Learn. Res. (JMLR) 20(96), 1–7 (2019)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.AptivKrakówPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland

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