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A Particle Swarm Intelligence based Fuzzy Time Series Forecasting Model

by Jilani T. A., Burney S.M.A., Amjad U., Tanveer A. Siddiqui
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 10
Year of Publication: 2012
Authors: Jilani T. A., Burney S.M.A., Amjad U., Tanveer A. Siddiqui
10.5120/4742-6776

Jilani T. A., Burney S.M.A., Amjad U., Tanveer A. Siddiqui . A Particle Swarm Intelligence based Fuzzy Time Series Forecasting Model. International Journal of Computer Applications. 38, 10 ( January 2012), 47-52. DOI=10.5120/4742-6776

@article{ 10.5120/4742-6776,
author = { Jilani T. A., Burney S.M.A., Amjad U., Tanveer A. Siddiqui },
title = { A Particle Swarm Intelligence based Fuzzy Time Series Forecasting Model },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 10 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 47-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number10/4742-6776/ },
doi = { 10.5120/4742-6776 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:10.477215+05:30
%A Jilani T. A.
%A Burney S.M.A.
%A Amjad U.
%A Tanveer A. Siddiqui
%T A Particle Swarm Intelligence based Fuzzy Time Series Forecasting Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 10
%P 47-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we have presented a new particle swarm optimization based multivariate fuzzy time series forecasting method. This method assumes five-factors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods of fuzzy time series forecasting. Experimentally, it is proved that our proposed method perform better than many existing fuzzy time series forecasting methods. The interest of this paper centers in applying swarm intelligence approaches in forecasting related problems. The dataset is taken from National Institute of Statistics, Belgium.

References
  1. Q. Song and B. S. Chissom 1993a “Forecasting Enrollments with Fuzzy Time Series—Part I”, Fuzzy Sets and System, Vol. 54, No. 1, pp. 1–9.
  2. K. Huarng, 2001a “Heuristic models of fuzzy time series for forecasting,” Fuzzy Sets and Systems, Vol. 123, No. 3, pp. 369–386.
  3. S. M. Chen, 2002 “Forecasting Enrollments Based on High-Order Fuzzy Time Series”, Cybernetic Systems, Vol. 33, No. 1, pp. 1–16.
  4. L. W. Lee, L. W. Wang, S. M. Chen, Jun. 2006 “Handling Forecasting Problems Based on Two-Factors High-Order Time Series”, IEEE Transactions on Fuzzy Systems, Vol. 14, No. 3, pp.468-477.
  5. Melike Sah and Y. D. Konstsntin, 2004 “Forecasting Enrollment Model based on first-order fuzzy time series”, Published in proc., International Conference on Computational Intelligence, Istanbul, Turkey.
  6. T. A. Jilani, S. M. A. Burney and C. Ardil, 2007a “Fuzzy metric approach for fuzzy time series forecasting based on frequency density based partitioning”, International Journal of Computational Intelligence, Vol. 4, No. 2, pp 12-18.
  7. T. A. Jilani, S. M. A. Burney, 2007b “M-Factor High Order Fuzzy Time Series Forecasting for Road Accident Data”, Advances in Soft Computing vol. 41, pp.246-257.
  8. T. A. Jilani, S. M. A. Burney, 2008a “Multivariate High Order Fuzzy Time Series Forecasting for Car Road Accidents”, International Journal of Computational Intelligence, Vol. 4, No. 1, pp. 15-20.
  9. T. A. Jilani, S. M. A. Burney, 2008b “Multivariate stochastic fuzzy forecasting models”, Expert Systems With Applications, vol. 37 No. 2, pp. 691-700.
  10. H. J. Zimmerman, 2001 “Fuzzy set theory and its applications”, Kluwer Publishers, Boston, MA.
  11. H. Ishibuchi, R. Fujioka and H. Tanaka, 1993 "Neural Networks that Learn from Fuzzy If-Then Rules", IEEE Transactions on Fuzzy Systems, Vol. 1, No. 1, pp.85-97.
  12. G. J. Klir and B. Yuan, 2005 “Fuzzy Sets and Fuzzy Logic: Theory and Applications”, Prentice Hall, India, Ch. 4.
  13. R. R. Yager and P. P. D. Filev, 2002 “Essentials of Fuzzy Modeling and Control”, John Wiley and Sons, Inc..
  14. B. J. MacLennan, 2004, “Natural computation and non-turing models of computation”, Theoretical Computer Science, (1–3), pp. 115–145.
  15. P. Marrow, 2000 “Nature-inspired computing technology and applications”. BT Technology Journal (4), pp. 13–23.
  16. A. P. Deweijer, C. B. Lucasius, L. Buydens 1994, “Curve-fitting using natural computation”, Analytical Chemistry 66(1), pp. 23–31.
  17. J. Kennedy and R. Eberhart, 2001 “Swarm Intelligence”. Morgan Kaufmann Publishers, Inc., San Francisco, CA.
  18. Maurice Clerc, 2006, “Particle Swarm Optimization”, ISTE Ltd, USA.
  19. K. Huarng, 2001b “Effective Lengths of Intervals to Improve Forecasting in Fuzzy Time Series”, Fuzzy Sets System, Vol. 123, No. 3, pp. 387–394.
Index Terms

Computer Science
Information Sciences

Keywords

Average forecasting error rate (AFER) Fuzziness of fuzzy sets Fuzzy If-Then rule particle swarm optimization (PSO)

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