Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Information Technologies for Industry

IITI'18 2018: Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18) pp 382–389Cite as

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

  • Conference paper
  • First Online:

  • 316 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((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.

This is a preview of subscription content, 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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

References

  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. Box, G., Jenkins, G.: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco (1970)

    MATH  Google Scholar 

  3. Hwang, J.R., Chen, S.M., Lee, C.H.: Handling forecasting problems using fuzzy time series. Fuzzy Sets Syst. 100, 217–228 (1998)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  5. Perfilieva, I.: Fuzzy transforms: theory and applications. Fuzzy Sets Syst. 157, 993–1023 (2006)

    Article  MathSciNet  Google Scholar 

  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. Perfilieva, I., Danková, M., Bede, B.: Towards a higher degree F-transform. Fuzzy Sets Syst. 180, 3–19 (2011)

    Article  MathSciNet  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  9. Sarkar, M.: Ruggedness measures of medical time series using fuzzy-rough sets and fractals. Pattern Recognit. Lett. Arch. 27, 447–454 (2006)

    Article  Google Scholar 

  10. Song, Q., Chissom, B.: Fuzzy time series and its models. Fuzzy Sets Syst. 54, 269–277 (1993)

    Article  MathSciNet  Google Scholar 

  11. Song, Q., Chissom, B.: Forecasting enrollments with fuzzy time series. Part I. Fuzzy Sets Syst. 54, 1–9 (1993)

    Article  Google Scholar 

  12. Wold, H.: A Study in the Analysis of Stationary Time Series. Almqvist and Wiksel, Stockholm (1938)

    MATH  Google Scholar 

  13. Yarushkina, N.G.: Principles of the Theory of Fuzzy and Hybrid Systems. Finances and Statistics, Moscow (2004)

    Google Scholar 

  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. 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. Mendel, J.M., John, R.I.B.: Type-2 fuzzy sets made simple. IEEE Trans. Fuzzy Syst. 10(2), 117–127 (2002)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Ostrava, Czech Republic

    Anton Romanov & Irina Perfilieva

Authors
  1. Anton Romanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Irina Perfilieva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anton Romanov .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  3. Bauman Moscow State Technical University, Moscow, Russia

    Valery Tarassov

  4. VSB-Technical University of Ostrava, Ostrava, Czech Republic

    Vaclav Snasel

  5. Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Romanov, A., Perfilieva, I. (2019). Time Series Grouping Based on Fuzzy Sets and Fuzzy Sets Type 2. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-01818-4_38

Download citation

Publish with us

Policies and ethics

Search

Navigation