Skip to main content
SpringerLink
Log in

Time Series Encodings with Temporal Convolutional Networks

  • Conference paper
  • First Online:
Bioinspired Optimization Methods and Their Applications (BIOMA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12438))

Included in the following conference series:

Abstract

The training of anomaly detection models usually requires labeled data. We present in this paper a novel approach for anomaly detection in time series which trains unsupervised using a convolutional approach coupled to an autoencoder framework. After training, only a small amount of labeled data is needed to adjust the anomaly threshold. We show that our new approach outperforms several other state-of-the-art anomaly detection algorithms on a Mackey-Glass (MG) anomaly benchmark. At the same time our autoencoder is capable of learning interesting representations in latent space. Our new MG anomaly benchmark allows to create an unlimited amount of anomaly benchmark data with steerable difficulty. In this benchmark, the anomalies are well-defined, yet difficult to spot for the human eye.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    GitHub repository: https://github.com/MarkusThill/MGAB/.

References

  1. Ahmad, S.: Running swarms (2017). http://nupic.docs.numenta.org/0.6.0/guide-swarming.html. Accessed 29 June 2020

  2. Ansmann, G.: Efficiently and easily integrating differential equations with JiTCODE, JiTCDDE, and JiTCSDE. Chaos 28(4), 043116 (2018)

    Article  MathSciNet  Google Scholar 

  3. Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. CoRR abs/1803.01271 (2018)

    Google Scholar 

  4. Bergstra, J., et al.: Hyperopt: a Python library for model selection and hyperparameter optimization. Comput. Sci. Discov. 8(1), 014008 (2015)

    Article  Google Scholar 

  5. Chan, D.M., Rao, R., Huang, F., Canny, J.F.: GPU accelerated T-distributed stochastic neighbor embedding. JPDC 131, 1–13 (2019)

    Google Scholar 

  6. Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: ICML 2017, p. 933–941 (2017)

    Google Scholar 

  7. Fischer, M., et al.: Anomaly Detection on Time Series: An Evaluation of Deep Learning Methods (2019). https://github.com/KDD-OpenSource/DeepADoTS

  8. Gehring, J., Auli, M., Grangier, D., Yarats, D., Dauphin, Y.N.: Convolutional sequence to sequence learning. CoRR abs/1705.03122 (2017)

    Google Scholar 

  9. George, D., Hawkins, J.: Towards a mathematical theory of cortical micro-circuits. PLoS Comput. Biol. 5(10), e1000532 (2009)

    Article  MathSciNet  Google Scholar 

  10. Goldberger, A.L., et al.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)

    Article  Google Scholar 

  11. Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170–180. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46145-0_17

    Chapter  Google Scholar 

  12. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  13. Jiang, W., Hong, Y., Zhou, B., He, X.: A GAN-based anomaly detection approach for imbalanced industrial time series. IEEE Access 7, 143608–143619 (2019)

    Article  Google Scholar 

  14. Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A Convolutional neural network for modelling sentences. In: ACL, Baltimore, Maryland, pp. 655–665 (2014)

    Google Scholar 

  15. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  16. Laptev, N., Amizadeh, S.: Yahoo anomaly detection dataset S5 (2015). http://webscope.sandbox.yahoo.com/catalog.php?datatype=s&did=70

  17. Lavin, A., Ahmad, S.: Evaluating real-time anomaly detection algorithms - the Numenta anomaly benchmark. In: ICMLA (2015)

    Google Scholar 

  18. 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 

  19. Lin, M., Chen, Q., Yan, S.: Network in network. arXiv preprint arXiv:1312.4400 (2013)

  20. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)

    MATH  Google Scholar 

  21. Mackey, M.C., Glass, L.: Oscillation and chaos in physiological control systems. Science 197(4300), 287–289 (1977)

    Article  Google Scholar 

  22. Malhotra, P., et al.: LSTM-based encoder-decoder for multi-sensor anomaly detection. CoRR abs/1607.00148 (2016)

    Google Scholar 

  23. Munir, M., et al.: DeepAnT: a deep learning approach for unsupervised anomaly detection in time series. IEEE Access 7, 1991–2005 (2019)

    Article  Google Scholar 

  24. van den Oord, A., et al.: WaveNet: a generative model for raw audio. CoRR abs/1609.03499 (2016)

    Google Scholar 

  25. Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library. In: Wallach, H., et al. (eds.) NIPS, pp. 8024–8035. Curran Assoc. (2019)

    Google Scholar 

  26. Pereira, J., Silveira, M.: Unsupervised anomaly detection in energy time series data using variational recurrent autoencoders with attention. In: Wani, M.A., et al. (eds.) ICMLA, pp. 1275–1282. IEEE (2018)

    Google Scholar 

  27. Sölch, M., et al.: Variational inference for on-line anomaly detection in high-dimensional time series. CoRR abs/1602.07109 (2016)

    Google Scholar 

  28. Taylor, M., et al.: numenta/nupic: 1.0.5 (2018). https://doi.org/10.5281/zenodo.1257382

  29. Thill, M., Däubener, S., Konen, W., Bäck, T.: Anomaly detection in electrocardiogram readings with stacked LSTM networks. In: ITAT. CEUR Workshop Proceedings, vol. 2473, pp. 17–25 (2019)

    Google Scholar 

  30. Thill, M., Konen, W., Bäck, T.: Online anomaly detection on the Webscope S5 dataset: a comparative study. In: EAIS, pp. 1–8. IEEE (2017)

    Google Scholar 

  31. Thill, M., Konen, W., Bäck, T.: MGAB: The Mackey-Glass Anomaly Benchmark (2020). https://doi.org/10.5281/zenodo.3762385

  32. Xu, H., et al.: Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications. In: WWW, pp. 187–196 (2018)

    Google Scholar 

  33. Zong, B., et al.: Deep autoencoding Gaussian mixture model for unsupervised anomaly detection. In: ICLR (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. TH Köln – University of Applied Sciences, Gummersbach, Germany

    Markus Thill & Wolfgang Konen

  2. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

Authors
  1. Markus Thill
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wolfgang Konen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Thomas Bäck
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Markus Thill .

Editor information

Editors and Affiliations

  1. Jožef Stefan Institute, Ljubljana, Slovenia

    Bogdan Filipič

  2. University of Strathclyde, Glasgow, UK

    Edmondo Minisci

  3. University of Strathclyde, Glasgow, UK

    Massimiliano Vasile

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Thill, M., Konen, W., Bäck, T. (2020). Time Series Encodings with Temporal Convolutional Networks. In: Filipič, B., Minisci, E., Vasile, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science(), vol 12438. Springer, Cham. https://doi.org/10.1007/978-3-030-63710-1_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-63710-1_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63709-5

  • Online ISBN: 978-3-030-63710-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation