The Problem of the Anomaly Detection in Time Series Collections for Dynamic Objects

  • S. G. Antipov
  • V. N. Vagin
  • O. L. Morosin
  • M. V. Fomina
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 875)


In this paper the approaches to processing temporal data is considered. The problem of the anomaly detection among sets of time series is setting up. The algorithms TS-ADEEP and TS-ADEEP-Multi for anomaly detection in time series sets for the case when the learning set contains examples of several classes are proposed. The method for improving the accuracy of anomaly detection, due to “compression” of these time series is used.


Time series Inductive notion formation Exclusion detection Classification 



This work was supported by grants from the Russian Foundation for Basic Research № 15-01-05567, 17-07-00442.


  1. 1.
    Vagin, V., Golovina, E., Zagoryanskaya, A., Fomina, M.: Exact and plausible inference in intelligent systems. In: Vagin, V., Pospelov, D. (eds.) 712 p. FizMatLit, Moscow (2008). (in Russian)Google Scholar
  2. 2.
    Roddick, J.F., Spiliopoulou, M.: A bibliography of temporal, spatial and spatio-temporal data mining research. SIGKDD Explor. Newsl. 1(1), 34–38 (1999). Scholar
  3. 3.
    Lin, W., Orgun, M.A., Williams, G.J.: An overview of temporal data mining. In: Proceedings of the 1st Australasian Data Mining Workshop, Sydney, Australia, pp. 83–90 (2002)Google Scholar
  4. 4.
    Antunes, C.M., Oliveira, A.L.: Temporal data mining: an overview. In: Eleventh International Workshop on the Principles of Diagnosis (2001)Google Scholar
  5. 5.
    Perfilieva, L., Yarushkina, N., Afanasieva, T., Romanov, A.: Time series analysis using soft computing methods. Int. J. Gen. Syst. 42(6), 687–705 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection - a surey. ACM Comput. Surv. 41(3), 1–72 (2009)CrossRefGoogle Scholar
  7. 7.
    Arning, A., Agrawal, R., Raghavan P.: A linear method for deviation detection in large databases. In: Proceedings of KDD 1996, pp. 164–169 (1996)Google Scholar
  8. 8.
    Antipov, S., Fomina, M.: Problem of anomalies detection in time series sets. Prog. Prod. Syst. (2), 78–82 (2012). (in Russian)Google Scholar
  9. 9.
    Fomina, M., Antipov, S., Vagin, V.: Methods and algorithms of anomaly searching in collections of time series. In: Proceedings of the first International Scientific Conference Intelligent Information Technologies for Industry (IITI 2016), vol. 1, pp. 63–73. In Series Advances in Intelligent Systems and Computing, vol. 450. Springer (2016)Google Scholar
  10. 10.
    Saito, N.: Local feature extraction and its application using a library of bases. Ph.D. thesis, Yale University, December 1994. 244 pGoogle Scholar
  11. 11.
    Pham, D.T., Chan, A.B.: Control chart pattern recognition using a new type of self organizing neural network. Proc. Instn. Mech. Engrs. 212(1), 115–127 (1998)Google Scholar
  12. 12.
    UCI Repository of Machine Learning Datasets.
  13. 13.
    Antipov, S.G., Vagin, V.N., Fomina, M.V.: Detection of data anomalies at network traffic analysis. In: Open Semantic Technologies for Intelligent Systems - Conference Proceedings, Minsk, Belarus, pp. 195–198 (2017)Google Scholar
  14. 14.
    Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)Google Scholar
  15. 15.
    Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T., Zaccaria, R.: Analysis of human behavior recognition algorithms based on acceleration data. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1602–1607 (2013)Google Scholar
  16. 16.
    Yanping, C., Eamonn, K., Bing, H., et al.: The UCR Time Series Classification Archive–2015, July 2015.
  17. 17.
    Olszhewski, R.: Generalized Feature Extraction for Structural Pattern Recognition in Time-Series Data. Ph.D thesis. School of Computer Science, Carnegie Mellon University, Pittsburgh (2001). 125 pGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • S. G. Antipov
    • 1
  • V. N. Vagin
    • 1
  • O. L. Morosin
    • 1
  • M. V. Fomina
    • 1
  1. 1.Department of Application MathematicsNational Research University “MPEI”MoscowRussian Federation

Personalised recommendations