Skip to main content

Show Me Your Friends and I’ll Tell You Who You Are. Finding Anomalous Time Series by Conspicuous Cluster Transitions

Part of the Communications in Computer and Information Science book series (CCIS,volume 1127)

Abstract

The analysis of time series is an important field of research in data mining. This includes different sub areas like trend analysis, outlier detection, forecasting or simply the comparison of multiple time series. Clustering is also an equally important and vast field in time series analysis. Different clustering algorithms provide different analysis aspects like the detection of classes or outliers. There are various approaches how to apply cluster algorithms to time series. Previous work either extracted subsequences or feature sets as an input for cluster algorithms. A rarely used but important approach in clustering of time series is the grouping of data points per point in time. Based on this technique we present a method which analyses the transitions of time series between clusters over time. We evaluate our approach on multiple multivariate time series of different data sets. We discover conspicuous behaviors in relation to groups of sequences and provide a robust outlier detection algorithm.

Keywords

  • Outlier detection
  • Time series analysis
  • Clustering

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-15-1699-3_8
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   64.99
Price excludes VAT (USA)
  • ISBN: 978-981-15-1699-3
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   84.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

References

  1. Ahmad, S., Lavin, A., Purdy, S., Agha, Z.: Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262, 134–147 (2017)

    CrossRef  Google Scholar 

  2. Ahmar, A.S., et al.: Modeling data containing outliers using ARIMA additive outlier (ARIMA-AO). J. Phys: Conf. Ser. 954, 012010 (2018)

    Google Scholar 

  3. Banerjee, A., Ghosh, J.: Clickstream clustering using weighted longest common subsequences. In: Proceedings of the Web Mining Workshop at the 1st SIAM Conference on Data Mining, pp. 33–40 (2001)

    Google Scholar 

  4. Ben-David, S., Von Luxburg, U.: Relating clustering stability to properties of cluster boundaries. In: 21st Annual Conference on Learning Theory (COLT 2008), pp. 379–390 (2008)

    Google Scholar 

  5. Cheng, H., Tan, P.N., Potter, C., Klooster, S.: Detection and characterization of anomalies in multivariate time series. In: Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 413–424 (2009)

    Google Scholar 

  6. Cho, H., Fryzlewicz, P.: Multiple change-point detection for high-dimensional time series via sparsified binary segmentation (2014)

    MathSciNet  CrossRef  Google Scholar 

  7. Cleveland, R.B., Cleveland, W.S., McRae, J.E., Terpenning, I.: STL: a seasonal-trend decomposition procedure based on loess (with discussion). J. Official Stat. 6, 3–73 (1990)

    Google Scholar 

  8. ASA Statistics Computing and Graphics: Airline on-time performance. http://stat-computing.org/dataexpo/2009/the-data.html. Accessed 15 July 2019

  9. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)

    Google Scholar 

  10. Ferreira, L.N., Zhao, L.: Time series clustering via community detection in networks. Inf. Sci. 326, 227–242 (2016)

    MathSciNet  CrossRef  Google Scholar 

  11. Hill, D.J., Minsker, B.S.: Anomaly detection in streaming environmental sensor data: a data-driven modeling approach. Environ. Model Softw. 25(9), 1014–1022 (2010)

    CrossRef  Google Scholar 

  12. Huang, X., Ye, Y., Xiong, L., Lau, R.Y., Jiang, N., Wang, S.: Time series k-means: a new k-means type smooth subspace clustering for time series data. Inf. Sci. 367–368, 1–13 (2016)

    Google Scholar 

  13. Keogh, E., Lonardi, S., Chiu, B.Y.C.: Finding surprising patterns in a time series database in linear time and space. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2002, pp. 550–556 (2002)

    Google Scholar 

  14. Landauer, M., Wurzenberger, M., Skopik, F., Settanni, G., Filzmoser, P.: Time series analysis: unsupervised anomaly detection beyond outlier detection. In: Su, C., Kikuchi, H. (eds.) ISPEC 2018. LNCS, vol. 11125, pp. 19–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99807-7_2

    CrossRef  Google Scholar 

  15. Lin, J., Keogh, E., Fu, A., Van Herle, H.: Approximations to magic: finding unusual medical time series. In: 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), pp. 329–334 (2005)

    Google Scholar 

  16. Liu, S., Yamada, M., Collier, N., Sugiyama, M.: Change-point detection in time-series data by relative density-ratio estimation. Neural Netw. 43, 72–83 (2013)

    CrossRef  Google Scholar 

  17. Malhotra, P., Vig, L., Shroff, G.M., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: ESANN (2015)

    Google Scholar 

  18. Munir, M., Siddiqui, S.A., Chattha, M.A., Dengel, A., Ahmed, S.: FuseAD: unsupervised anomaly detection in streaming sensors data by fusing statistical and deep learning models. Sensors 19(11), 2451 (2019)

    CrossRef  Google Scholar 

  19. Paparrizos, J., Gravano, L.: k-shape: efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1855–1870 (2015)

    Google Scholar 

  20. Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561–580 (2007)

    CrossRef  Google Scholar 

  21. Sun, P., Chawla, S., Arunasalam, B.: Mining for outliers in sequential databases. In: ICDM, pp. 94–106 (2006)

    Google Scholar 

  22. Truong, C.D., Anh, D.T.: A novel clustering-based method for time series motif discovery under time warping measure. Int. J. Data Sci. Anal. 4(2), 113–126 (2017)

    CrossRef  Google Scholar 

  23. Zhou, Y., Zou, H., Arghandeh, R., Gu, W., Spanos, C.J.: Non-parametric outliers detection in multiple time series a case study: power grid data analysis. In: AAAI (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Martha Tatusch .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Tatusch, M., Klassen, G., Bravidor, M., Conrad, S. (2019). Show Me Your Friends and I’ll Tell You Who You Are. Finding Anomalous Time Series by Conspicuous Cluster Transitions. In: , et al. Data Mining. AusDM 2019. Communications in Computer and Information Science, vol 1127. Springer, Singapore. https://doi.org/10.1007/978-981-15-1699-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1699-3_8

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1698-6

  • Online ISBN: 978-981-15-1699-3

  • eBook Packages: Computer ScienceComputer Science (R0)