Skip to main content
SpringerLink
Log in

ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection

  • Conference paper
  • First Online:
Advances in Intelligent Data Analysis XXI (IDA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13876))

Included in the following conference series:

  • 1166 Accesses

Abstract

The analysis of time series data is of high relevance in fields like manufacturing, health, automotive, or science. In this paper, we propose ROCKAD, a kernel-based approach for semi-supervised whole time series anomaly detection, i.e. the assignment of a single anomaly score to an entire time series. Our key idea is to use ROCKET as an unsupervised feature extractor and to train a single as well as an ensemble of k-nearest neighbors anomaly detectors to deduce an anomaly score. To the best of our knowledge, this is the first approach to transfer the ideas of ROCKET to the task of anomaly detection. We systematically evaluate ROCKAD for univariate time series and show it is statistically significantly better compared to baseline methods. Additionally, we show in a case study that ROCKAD is also applicable to multivariate time series.

A. Theissler, M. Wengert and F. Gerschner—contributed equally.

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 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

Notes

  1. 1.

    ROCKAD source code and further information: https://ml-and-vis.org/rockad.

References

  1. Abulibdeh, A.: Time series analysis of environmental quality in the state of Qatar. Energy Policy 168, 113089 (2022)

    Article  Google Scholar 

  2. Ahmad, A., Song, C., Tan, R., Gärtler, M., Klöpper, B.: Active learning application for recognizing steps in chemical batch production. In: 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–4. IEEE (2022)

    Google Scholar 

  3. Atzmueller, M., Hayat, N., Schmidt, A., Klöpper, B.: Explanation-aware feature selection using symbolic time series abstraction: approaches and experiences in a petro-chemical production context. In: 2017 IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 799–804. IEEE (2017)

    Google Scholar 

  4. Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. ArXiv (2018)

    Google Scholar 

  5. Beggel, L., Kausler, B.X., Schiegg, M., Pfeiffer, M., Bischl, B.: Time series anomaly detection based on shapelet learning. Comput. Stat. 34, 945–976 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  6. Benavoli, A., Corani, G., Mangili, F.: Should we really use post-hoc tests based on mean-ranks? J. Mach. Learn. Res. 17, 1–10 (2016)

    MathSciNet  MATH  Google Scholar 

  7. Blázquez-García, A., Conde, A., Mori, U., Lozano, J.A.: A review on outlier/anomaly detection in time series data. ACM Comput. Surv. (CSUR) 54(3), 1–33 (2021)

    Article  Google Scholar 

  8. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: ACM SIGMOD International conference on Management of data, pp. 93–104 (2000)

    Google Scholar 

  9. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)

    Google Scholar 

  10. Chandola, V., Cheboli, D., Kumar, V.: Detecting anomalies in a time series database (2009)

    Google Scholar 

  11. Christ, M., Braun, N., Neuffer, J., Kempa-Liehr, A.W.: Time series FeatuRe extraction on basis of scalable hypothesis tests (tsfresh – a python package). Neurocomputing 307, 72–77 (2018)

    Article  Google Scholar 

  12. Dau, H.A., et al.: The UCR time series archive. IEEE/CAA J. Automatica Sinica 6(6), 1293–1305 (2019)

    Article  Google Scholar 

  13. Dempster, A., Petitjean, F., Webb, G.I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. CoRR abs/1910.13051 (2019)

    Google Scholar 

  14. Dempster, A., Petitjean, F., Webb, G.I.: ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Min. Knowl. Disc. 34(5), 1454–1495 (2020). https://doi.org/10.1007/s10618-020-00701-z

    Article  MathSciNet  MATH  Google Scholar 

  15. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)

    MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  17. Hsu, C.Y., Liu, W.C.: Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing. J. Intell. Manuf. 32(3), 823–836 (2021). https://doi.org/10.1007/s10845-020-01591-0

    Article  MathSciNet  Google Scholar 

  18. Li, Y., Zha, D., Zou, N., Hu, X.: PyODDS: an end-to-end outlier detection system (2019)

    Google Scholar 

  19. Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 413–422 (2008)

    Google Scholar 

  20. Markert, T., Matich, S., Hoerner, E., Theissler, A., Atzmueller, M.: Fingertip 6-axis force/torque sensing for texture recognition in robotic manipulation. In: International Conference on Emerging Technologies and Factory Automation (ETFA). IEEE (2021)

    Google Scholar 

  21. Seliya, N., Abdollah Zadeh, A., Khoshgoftaar, T.M.: A literature review on one-class classification and its potential applications in big data. J. Big Data 8(1), 1–31 (2021). https://doi.org/10.1186/s40537-021-00514-x

    Article  Google Scholar 

  22. Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  23. Raab, D., Theissler, A., Spiliopoulou, M.: XAI4EEG: spectral and spatio-temporal explanation of deep learning-based seizure detection in EEG time series. Neural Comput. Appl., pp. 1–18 (2022). https://doi.org/10.1007/s00521-022-07809-x

  24. Schmidl, S., Wenig, P., Papenbrock, T.: Anomaly detection in time series: a comprehensive evaluation. Proc. VLDB Endowment 15(9), 1779–1797 (2022)

    Article  Google Scholar 

  25. Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13, 1443–1471 (2001)

    Article  MATH  Google Scholar 

  26. Steinbuss, G., Böhm, K.: Generating artificial outliers in the absence of genuine ones - a survey. ACM Trans. Knowl. Disc. Data 15(2), 1–37 (2021)

    Article  Google Scholar 

  27. Sun, J., et al.: A hybrid deep neural network for classification of schizophrenia using EEG Data. Sci. Rep. 11(1), 4706 (2021). https://doi.org/10.1038/s41598-021-83350-6

    Article  Google Scholar 

  28. Tax, D.M.: One-class classification. Concept-learning in the absence of counter-examples. Ph.D. thesis, Delft University of Technology (2001)

    Google Scholar 

  29. Teh, H.Y., Kevin, I., Wang, K., Kempa-Liehr, A.W.: Expect the unexpected: unsupervised feature selection for automated sensor anomaly detection. IEEE Sens. J. 21(16), 18033–18046 (2021)

    Article  Google Scholar 

  30. Teng, M.: Anomaly detection on time series. In: 2010 IEEE International Conference on Progress in Informatics and Computing, vol. 1, pp. 603–608 (2010)

    Google Scholar 

  31. Theissler, A.: Detecting anomalies in multivariate time series from automotive systems. Ph.D. thesis, Brunel University London (2013)

    Google Scholar 

  32. Theissler, A.: Detecting known and unknown faults in automotive systems using ensemble-based anomaly detection. Knowl.-Based Syst. 123(C), 163–173 (2017)

    Google Scholar 

  33. Theissler, A., Kraft, A.L., Rudeck, M., Erlenbusch, F.: VIAL-AD: visual interactive labelling for anomaly detection - an approach and open research questions. In: International Workshop on Interactive Adaptive Learning (IAL). CEUR-WS (2020)

    Google Scholar 

  34. Theissler, A., Pérez-Velázquez, J., Kettelgerdes, M., Elger, G.: Predictive maintenance enabled by machine learning: use cases and challenges in the automotive industry. Reliab. Eng. Syst. Saf. 215, 107864 (2021)

    Article  Google Scholar 

  35. Theissler, A., Thomas, M., Burch, M., Gerschner, F.: ConfusionVis: comparative evaluation and selection of multi-class classifiers based on confusion matrices. Knowl.-Based Syst. 247, 108651 (2022)

    Article  Google Scholar 

  36. Thill, M., Konen, W., Bäck, T.: Time series encodings with temporal convolutional networks. In: Filipič, B., Minisci, E., Vasile, M. (eds.) BIOMA 2020. LNCS, vol. 12438, pp. 161–173. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63710-1_13

    Chapter  Google Scholar 

  37. Trittenbach, H., Böhm, K., Assent, I.: Active learning of SVDD hyperparameter values. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 109–117 (2020)

    Google Scholar 

  38. Ye, L., Keogh, E.: Time series shapelets. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM (2009)

    Google Scholar 

  39. Yeo, I.K., Johnson, R.: A new family of power transformations to improve normality or symmetry. Biometrika 87, 954–959 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  40. Zhai, S., Cheng, Y., Lu, W., Zhang, Z.: Deep structured energy based models for anomaly detection. In: International Conference on Machine Learning, pp. 1100–1109. PMLR (2016)

    Google Scholar 

  41. Zhang, J., Zeng, B., Shen, W., Gao, L.: A one-class Shapelet dictionary learning method for wind turbine bearing anomaly detection. Measurement 197, 111318 (2022)

    Article  Google Scholar 

  42. Zong, B., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Aalen University of Applied Sciences, 73430, Aalen, Germany

    Andreas Theissler, Manuel Wengert & Felix Gerschner

Authors
  1. Andreas Theissler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manuel Wengert
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Felix Gerschner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Theissler .

Editor information

Editors and Affiliations

  1. Université de Caen Normandie, Caen, France

    Bruno Crémilleux

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Sibylle Hess

  3. UCLouvain, Louvain-la-Neuve, Belgium

    Siegfried Nijssen

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Theissler, A., Wengert, M., Gerschner, F. (2023). ROCKAD: Transferring ROCKET to Whole Time Series Anomaly Detection. In: Crémilleux, B., Hess, S., Nijssen, S. (eds) Advances in Intelligent Data Analysis XXI. IDA 2023. Lecture Notes in Computer Science, vol 13876. Springer, Cham. https://doi.org/10.1007/978-3-031-30047-9_33

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-30047-9_33

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30046-2

  • Online ISBN: 978-3-031-30047-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation