Skip to main content
Springer Nature Link
Log in

Fuzzy Time Series Prediction Model

  • Conference paper
Information Intelligence, Systems, Technology and Management (ICISTM 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 141))

Abstract

The main objective to design this proposed model is to overcome the drawbacks of the exiting approaches and derive more robust & accurate methodology to forecast data. This innovative soft computing time series model is designed by joint consideration of three key points (1) Event discretization of time series data (2 Frequency density based partitioning (3) Optimizing fuzzy relationship in inventive way. As with most of cited papers, historical enrollment of university of Alabama is used in this study to illustrate the new forecasting process. Subsequently, the performance of the proposed model is demonstrated by making comparison with some of the pre-existing forecasting methods. In general, the findings of the study are interesting and superior in terms of least Average Forecasting Error Rate (AFER) and Mean Square Error (MSE) values.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Garg, B., Beg, M.M.S., Ansari, A.Q.: Inferential historical survey of time series predication using artificial neural network (2010)

    Google Scholar 

  2. Zadeh, L.A.: Fuzzy sets. Information and Control 8(3), 333–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  4. Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series Part I. Fuzzy Sets and Systems 54, 1–9

    Google Scholar 

  5. Song, Q., Chissom, B.S.: Forecasting enrollments with fuzzy time series: Part II. Fuzzy Sets and Systems 62, 1–8 (1994)

    Article  Google Scholar 

  6. Song, Q.: A note on fuzzy time series model selection with sample autocorrelation functions. Cybernetics and Systems: An International Journal 34, 93–107 (2003)

    Article  MATH  Google Scholar 

  7. Chen, S.M., Hsu, C.-C.: A new method to forecasting enrollments using fuzzy time series. International Journal of Applied Science and Engineering 2(3), 234–244 (2004)

    MathSciNet  Google Scholar 

  8. Chen, S.M.: Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems 81, 311–319 (1996)

    Article  Google Scholar 

  9. Chen, S.M.: Forecasting enrollments based on high-order fuzzy time series. Cybernetics and Systems: An International Journal 33, 1–16 (2002)

    Article  MATH  Google Scholar 

  10. Huarng, K.: Heuristic models of fuzzy time series for forecasting. Fuzzy Sets and Systems 123, 369–386 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  11. Huarng, K.: Effective lengths of intervals to improve forecasting in fuzzy time series. Fuzzy Sets and Systems 12, 387–394 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  12. Hsu, C.C., Chen, S.M.: A new method for forecasting enrollments based on fuzzy time series. In: Proceedings of the Seventh Conference on Artificial Intelligence and Applications, Taichung, Taiwan, Republic of China, pp. 17–22

    Google Scholar 

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

    Article  Google Scholar 

  14. Lee, L.W., Wang, L.W., Chen, S.M.: Handling forecasting problems based on two-factors high-order time series. IEEE Transactions on Fuzzy Systems 14(3), 468–477 (2006)

    Article  Google Scholar 

  15. Li, H., Kozma, R.: A dynamic neural network method for time series prediction using the KIII model. In: Proceedings of the 2003 International Joint Conference on Neural Networks, vol. 1, pp. 347–352 (2003)

    Google Scholar 

  16. Melike, S., Degtiarev, K.Y.: Forecasting enrollment model based on first-order fuzzy time series. In: Proceedings of World Academy of Science, Engineering and Technology, vol. 1, pp. 1307–6884 (2005)

    Google Scholar 

  17. Jilani, T.A., Burney, S.M.A., Ardil, C.: Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning. In: Proceedings of World Academy of Science, Engineering and Technolog, vol. 23, pp. 333–338 (2007)

    Google Scholar 

  18. Jilani, T.A., Burney, S.M.A., Ardil, C.: Multivariate high order fuzzy time series forecasting for car road accidents. International Journal of Computational Intelligence 4(1), 15–20 (2007)

    Google Scholar 

  19. Singh, S.R.: A computational method of forecasting based on fuzzy time series. International Journal of Mathematics and Computers in Simulation 79, 539–554 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  20. Stevenson, M., Porter, J.E.: Fuzzy time series forecasting using percentage change as the universe of discourse. In: Proceedings of World Academy of Science, Engineering and Technology, vol. 55, pp. 154–157 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    Bindu Garg, M. M. Sufyan Beg & B. M. Imran

  2. Department of Electrical Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    A. Q. Ansari

Authors
  1. Bindu Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. M. Sufyan Beg
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. A. Q. Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. M. Imran
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science, College of Engineering and Science, Louisiana Tech University, 71272, Ruston, LA, USA

    Sumeet Dua

  2. CISE Department, CSE 301, University of Florida, 32611, Gainesville, FL, USA

    Sartaj Sahni

  3. Management Development Institute, 122 007, Sukhrali, Gurgaon, India

    D. P. Goyal

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Garg, B., Sufyan Beg, M.M., Ansari, A.Q., Imran, B.M. (2011). Fuzzy Time Series Prediction Model. In: Dua, S., Sahni, S., Goyal, D.P. (eds) Information Intelligence, Systems, Technology and Management. ICISTM 2011. Communications in Computer and Information Science, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19423-8_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19423-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19422-1

  • Online ISBN: 978-3-642-19423-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics