Advertisement

Time Series Grouping Based on Fuzzy Sets and Fuzzy Sets Type 2

  • Anton Romanov
  • Irina Perfilieva
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

Abstract

The contribution is focused on a new method of grouping time series according to their local tendency indicator that is expressed by a linear coefficient of the \(F^1\)-transform. The useful consequence of grouping is an effective procedure of forecasting such that only one time series from a group is forecasted. Our approach for the analysis and forecasting of the time series of software development is used.

Notes

Acknowledgements

The authors acknowledge that the work was supported by the framework of the state task of the Ministry of Education and Science of the Russian Federation No. 2.1182.2017/4.6 “Development of methods and means for automating the production and technological preparation of aggregate-assembly aircraft production in the conditions of a multi-product production program” and RFFI-16-47-732070.

References

  1. 1.
    Moshkin, V.S.: Intelligent data analysis and ontological approach in project management. In: Moshkin, V.S., Pirogov, A.N., Timina, I.A., Shishkin, V.V., Yarushkina, N.G. (eds.) Automation of Management Processes, vol. 4, no. 46, pp. 84–92 (2016). (in Russian)Google Scholar
  2. 2.
    Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)zbMATHGoogle Scholar
  3. 3.
    Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst. 100, 217–228 (1998)CrossRefGoogle Scholar
  4. 4.
    Novák, V., Štěpnićka, M., Dvořák, A., Perfilieva, I., Pavliska, V.: Analysis of seasonal time series using fuzzy approach. Int. J. Gen. Syst. 39, 305–328 (2010)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Perfilieva, I.: Fuzzy transforms: theory and applications. Fuzzy Sets Syst. 157, 993–1023 (2006)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Perfilieva, I.: Fuzzy transforms: a challenge to conventional transforms. In: Hawkes, P.W. (ed.) Advances in Images and Electron Physics, vol. 147. Elsevier Academic Press, San Diego (2007)Google Scholar
  7. 7.
    Perfilieva, I., Danková, M., Bede, B.: Towards a higher degree F-transform. Fuzzy Sets Syst. 180, 3–19 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Perfilieva, I., Yarushkina, N., Afanasieva, T., Romanov, A.: Time series analysis using soft computing methods. Int. J. Gen. Syst. 42(6), 687–705 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Sarkar, M.: Ruggedness measures of medical time series using fuzzy-rough sets and fractals. Pattern Recognit. Lett. Arch. 27, 447–454 (2006)CrossRefGoogle Scholar
  10. 10.
    Song, Q., Chissom, B.: Fuzzy time series and its models. Fuzzy Sets Syst. 54, 269–277 (1993)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Song, Q., Chissom, B.: Forecasting enrollments with fuzzy time series. Part I. Fuzzy Sets Syst. 54, 1–9 (1993)CrossRefGoogle Scholar
  12. 12.
    Wold, H.: A Study in the Analysis of Stationary Time Series. Almqvist and Wiksel, Stockholm (1938)zbMATHGoogle Scholar
  13. 13.
    Yarushkina, N.G.: Principles of the Theory of Fuzzy and Hybrid Systems. Finances and Statistics, Moscow (2004)Google Scholar
  14. 14.
    Romanov A.A., Yarushkina N.G., Perfilieva, I.: Time series grouping on the basis of F1-transform. In: IEEE International Conference on Fuzzy Systems, pp. 517–521 (2014)Google Scholar
  15. 15.
    Yarushkina, N., Afanasieva, T., Igonin, A., Romanov, A., Shishkina, V., Perfilieva, I.: Time series processing and forecasting using soft computing tools. Lecture Notes in Computer Science, vol. 6743, pp. 155–162 (2011)Google Scholar
  16. 16.
    Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute for Research and Applications of Fuzzy ModelingUniversity of OstravaOstravaCzech Republic

Personalised recommendations