Fuzzy Time Series Forecasting Using Percentage Change as the Universe of Discourse
Since the pioneering work of Zadeh, fuzzy set theory has been applied to a myriad of areas. Song and Chissom introduced the concept of fuzzy time series and applied some methods to the enrollments of the University of Alabama. In recent years, a number of techniques have been proposed for forecasting based on fuzzy set theory methods. These methods have either used enrollment numbers or differences of enrollments as the universe of discourse. We propose using the year to year percentage change as the universe of discourse. In this communication, the approach of Jilani, Burney, and Ardil is modified by using the year to year percentage change as the universe of discourse. We use enrollment figures for the University of Alabama to illustrate our proposed method. The proposed method results in better forecasting accuracy than existing models.
[1] Chen, S. M. 1996. Forecasting enrollments based on fuzzy time series,
Fuzzy Sets and Systems, 81: 311-319.
[2] S. M. Chen, Forecasting enrollments based on high-order fuzzy time
series, Cybernetics and Systems: An International Journal, Vol. 33: pp.
1-16, 2002
[3] Chen, S. M. and Hsu, C.-C. 2004. A new method to forecasting enrollment using fuzzy time series, International Journal of Applied Science
and Engineering, 2, 3: 234-244.
[4] S. M. Chen, J. R. Hwang, Temperature prediction using fuzzy time
series, IEEE Transactions on Systems, Man, and Cybernetics-Part B:
Cybernetics, Vol. 30, pp.263-275, 2000.
[5] K. Huarng, Heuristic models of fuzzy time series for forecasting, Fuzzy
Sets and Systems, Vol. 123, pp. 369-386, 2002.
[6] K. Huarng, Effective lengths of intervals to improve forecasting in fuzzy
time series, Fuzzy Sets and Systems, Vol. 12, pp. 387-394, 2001.
[7] J. R. Hwang, S. M. Chen, C. H. Lee, Handling forecasting problems
using fuzzy time series, Fuzzy Sets and Systems, Vol. 100, pp. 217-228,
1998.
[8] T. A. Jilani, S. M. A. Burney, M-factor high order fuzzy time series
forecasting for road accident data, In IEEE-IFSA 2007, World Congress,
Cancun, Mexico, June 18-21, Forthcoming in Book series Advances in
Soft Computing, Springer-Verlag, 2007.
[9] T. A. Jilani, S. M. A. Burney, C. Ardil, Fuzzy Metric Approach for Fuzzy
Time Series Forecasting based on Frequency Density Based Partitioning,
Proceedings of World Academy of Science, Engineering and Technology,
Vol. 23, pp.333-338., 2007.
[10] T. A. Jilani, S. M. A. Burney, C. Ardil, Multivariate high order fuzzy
time series forecasting for car road accidents, International Journal of Computational Intelligence, Vol. 4, No. 1, pp.15-20., 2007.
[11] G. J. Klir, T. A. Folger, Fuzzy Sets, Uncertainty, and Information,
Prentice-Hall, New Jersey, U.S.A, 1988.
[12] G. J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications,
Prentice Hall, New Jersey, U.S.A, 2005.
[13] L. W. Lee, L. W. Wang, S. M. Chen, Handling forecasting problems
based on two-factors high-order time series, IEEE Transactions on Fuzzy
Systems, Vol. 14, No. 3, pp.468-477, 2006.
[14] H. Li, R. Kozma, A dynamic neural network method for time series
prediction using the KIII model, Proceedings of the 2003 International
Joint Conference on Neural Networks, 1: 347-352, 2003.
[15] S. Melike, K. Y. Degtiarev, Forecasting Enrollment Model Based on
First-Order Fuzzy Time Series, Proceedings of World Academy of Science,
Engineering and Technology, Vol. 1, pp. 1307-6884, 2005.
[16] S. Melike, Y. D. Konstsntin, Forecasting enrollment model based on first-order fuzzy time series, in proc. International Conference on Computational
Intelligence, Istanbul, Turkey, 2004.
[17] Q. Song, B. S. Chissom, Fuzzy time series and its models, Fuzzy Sets
and Systems, Vol. 54, pp. 269-277, 1993.
[18] Q. Song, B. S. Chissom, Forecasting enrollments with fuzzy time series
Part I, Fuzzy Sets and Systems, 54: 1-9.
[19] Q. Song, B. S. Chissom, Forecasting enrollments with fuzzy time series:
Part II, Fuzzy Sets and Systems, Vol. 62: pp. 1-8, 1994.
[1] Chen, S. M. 1996. Forecasting enrollments based on fuzzy time series,
Fuzzy Sets and Systems, 81: 311-319.
[2] S. M. Chen, Forecasting enrollments based on high-order fuzzy time
series, Cybernetics and Systems: An International Journal, Vol. 33: pp.
1-16, 2002
[3] Chen, S. M. and Hsu, C.-C. 2004. A new method to forecasting enrollment using fuzzy time series, International Journal of Applied Science
and Engineering, 2, 3: 234-244.
[4] S. M. Chen, J. R. Hwang, Temperature prediction using fuzzy time
series, IEEE Transactions on Systems, Man, and Cybernetics-Part B:
Cybernetics, Vol. 30, pp.263-275, 2000.
[5] K. Huarng, Heuristic models of fuzzy time series for forecasting, Fuzzy
Sets and Systems, Vol. 123, pp. 369-386, 2002.
[6] K. Huarng, Effective lengths of intervals to improve forecasting in fuzzy
time series, Fuzzy Sets and Systems, Vol. 12, pp. 387-394, 2001.
[7] J. R. Hwang, S. M. Chen, C. H. Lee, Handling forecasting problems
using fuzzy time series, Fuzzy Sets and Systems, Vol. 100, pp. 217-228,
1998.
[8] T. A. Jilani, S. M. A. Burney, M-factor high order fuzzy time series
forecasting for road accident data, In IEEE-IFSA 2007, World Congress,
Cancun, Mexico, June 18-21, Forthcoming in Book series Advances in
Soft Computing, Springer-Verlag, 2007.
[9] T. A. Jilani, S. M. A. Burney, C. Ardil, Fuzzy Metric Approach for Fuzzy
Time Series Forecasting based on Frequency Density Based Partitioning,
Proceedings of World Academy of Science, Engineering and Technology,
Vol. 23, pp.333-338., 2007.
[10] T. A. Jilani, S. M. A. Burney, C. Ardil, Multivariate high order fuzzy
time series forecasting for car road accidents, International Journal of Computational Intelligence, Vol. 4, No. 1, pp.15-20., 2007.
[11] G. J. Klir, T. A. Folger, Fuzzy Sets, Uncertainty, and Information,
Prentice-Hall, New Jersey, U.S.A, 1988.
[12] G. J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications,
Prentice Hall, New Jersey, U.S.A, 2005.
[13] L. W. Lee, L. W. Wang, S. M. Chen, Handling forecasting problems
based on two-factors high-order time series, IEEE Transactions on Fuzzy
Systems, Vol. 14, No. 3, pp.468-477, 2006.
[14] H. Li, R. Kozma, A dynamic neural network method for time series
prediction using the KIII model, Proceedings of the 2003 International
Joint Conference on Neural Networks, 1: 347-352, 2003.
[15] S. Melike, K. Y. Degtiarev, Forecasting Enrollment Model Based on
First-Order Fuzzy Time Series, Proceedings of World Academy of Science,
Engineering and Technology, Vol. 1, pp. 1307-6884, 2005.
[16] S. Melike, Y. D. Konstsntin, Forecasting enrollment model based on first-order fuzzy time series, in proc. International Conference on Computational
Intelligence, Istanbul, Turkey, 2004.
[17] Q. Song, B. S. Chissom, Fuzzy time series and its models, Fuzzy Sets
and Systems, Vol. 54, pp. 269-277, 1993.
[18] Q. Song, B. S. Chissom, Forecasting enrollments with fuzzy time series
Part I, Fuzzy Sets and Systems, 54: 1-9.
[19] Q. Song, B. S. Chissom, Forecasting enrollments with fuzzy time series:
Part II, Fuzzy Sets and Systems, Vol. 62: pp. 1-8, 1994.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:57041", author = "Meredith Stevenson and John E. Porter", title = "Fuzzy Time Series Forecasting Using Percentage Change as the Universe of Discourse", abstract = "Since the pioneering work of Zadeh, fuzzy set theory has been applied to a myriad of areas. Song and Chissom introduced the concept of fuzzy time series and applied some methods to the enrollments of the University of Alabama. In recent years, a number of techniques have been proposed for forecasting based on fuzzy set theory methods. These methods have either used enrollment numbers or differences of enrollments as the universe of discourse. We propose using the year to year percentage change as the universe of discourse. In this communication, the approach of Jilani, Burney, and Ardil is modified by using the year to year percentage change as the universe of discourse. We use enrollment figures for the University of Alabama to illustrate our proposed method. The proposed method results in better forecasting accuracy than existing models.
", keywords = "Fuzzy forecasting, fuzzy time series, fuzzified enrollments,time-invariant model", volume = "3", number = "7", pages = "499-4", }
