A New Quantile Based Fuzzy Time Series Forecasting Model
Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.
[1] Q. Song, and B. S. Chissom, "Forecasting enrollments with fuzzy time
series ÔÇö Part I," Fuzzy Sets and Systems, vol. 54, issue 1, 1993a, pp. 1-
9.
[2] B. S. Chissom, "Fuzzy time series and its models," Fuzzy Sets and
Systems, vol. 54, issue 3, 1993b, pp. 269-277.
[3] Q. Song, and B. S. Chissom, "Forecasting enrollments with fuzzy time
series ÔÇö Part II," Fuzzy Sets and Systems, vol. 62, 1994, pp. 1-8.
[4] S. M. Chen, "Forecasting enrollments based on fuzzy time series,"
Fuzzy Sets and Systems, vol. 81, 1996, pp. 311-319.
[5] Q. Song, "A note on fuzzy time series model selection with sample
autocorrelation functions," Cybernetics and Systems: An International
Journal, vol. 34, 2003, pp. 93-107.
[6] Q. Song, and R.P. Leland, "Adaptive learning defuzzification
techniques and applications," Fuzzy Sets and Systems, vol. 81, 1996,
pp. 321-329.
[7] J. R. Hwang, S. M. Chen, and C. H. Lee, "Handling forecasting
problems using fuzzy time series", Fuzzy Sets and Systems, vol. 100,
1998, pp. 217-228.
[8] K. Huarng, "Heuristic models of fuzzy time series for forecasting,"
Fuzzy Sets and Systems, vol. 123, issue 3, 2001a, pp. 369-386
[9] K. Huarng, "Effective lengths of intervals to improve forecasting in
fuzzy time series," Fuzzy Sets and Systems, vol. 123, issue 3, 2001b,
pp. 387-394.
[10] S.-M. Chen, "Forecasting enrollments based on high-order fuzzy time
series," Cybernetics and Systems: An International Journal, vol. 33,
2002, pp. 1-16.
[11] S.-M. Chen, and J.-R. Hwang, "Temperature prediction using fuzzy
time series, IEEE Transactions on Systems, Man, and Cybernetics ÔÇö
Part B," Cybernetics, vol. 30, 2000, pp. 263-275.
[12] R. -C. Tsaur, J. -C. O. Yang, and H. -F. Wang, "Fuzzy relation analysis
in fuzzy time series model," Computers and Mathematics with
Applications, vol. 49, 2005, pp. 539-548.
[13] H. Li, and R. Kozma, "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, 2003, pp.
347-352.
[14] S. F. Su, and S. H. Li, "Neural network based fusion of global and local
information in predicting time series," In proceedings of the 2003 IEEE
International Joint Conference on Systems, Man and Cybernetics, vol. 5,
2003, pp. 4445-4450.
[15] J. Sullivan, and W. H. Woodall, "A comparison of fuzzy forecasting and
Markov modeling," Fuzzy Sets and Systems, vol. 64, 1996, pp. 279-
293.
[16] L. -W. Lee, L. -W. Wang, S. -M. Chen, and Y.-H. Leu, "Handling
forecasting problems based on two-factor high-order time series," IEEE
Transactions on Fuzzy Systems, vol. 14, issue 3, 2006, pp. 468-477.
[17] T. A. Jilani, S. M. A. Burney, and C. Ardil, "Multivariate high order
fuzzy time series forecasting for car road accidents," International
Journal of Computational Intelligence, vol. 4, issue 1, 2007b, pp. 15-20.
[18] T. A. Jilani, and 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, 2007. Forthcoming in Book
series Advances in Soft Computing, Springer-Verlag, 2007a.
[19] S. -T. Li, and Y. -C. Cheng, "Deterministic fuzzy time series model for
forecasting enrollments," Computers and Mathematics with
Applications, vol. 53, 2007, pp. 1904-1920.
[20] L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, 1996, pp.
338-353.
[21] R. Koenker, "Quantile Regression", Cambridge University Press, NY-
2005.
[1] Q. Song, and B. S. Chissom, "Forecasting enrollments with fuzzy time
series ÔÇö Part I," Fuzzy Sets and Systems, vol. 54, issue 1, 1993a, pp. 1-
9.
[2] B. S. Chissom, "Fuzzy time series and its models," Fuzzy Sets and
Systems, vol. 54, issue 3, 1993b, pp. 269-277.
[3] Q. Song, and B. S. Chissom, "Forecasting enrollments with fuzzy time
series ÔÇö Part II," Fuzzy Sets and Systems, vol. 62, 1994, pp. 1-8.
[4] S. M. Chen, "Forecasting enrollments based on fuzzy time series,"
Fuzzy Sets and Systems, vol. 81, 1996, pp. 311-319.
[5] Q. Song, "A note on fuzzy time series model selection with sample
autocorrelation functions," Cybernetics and Systems: An International
Journal, vol. 34, 2003, pp. 93-107.
[6] Q. Song, and R.P. Leland, "Adaptive learning defuzzification
techniques and applications," Fuzzy Sets and Systems, vol. 81, 1996,
pp. 321-329.
[7] J. R. Hwang, S. M. Chen, and C. H. Lee, "Handling forecasting
problems using fuzzy time series", Fuzzy Sets and Systems, vol. 100,
1998, pp. 217-228.
[8] K. Huarng, "Heuristic models of fuzzy time series for forecasting,"
Fuzzy Sets and Systems, vol. 123, issue 3, 2001a, pp. 369-386
[9] K. Huarng, "Effective lengths of intervals to improve forecasting in
fuzzy time series," Fuzzy Sets and Systems, vol. 123, issue 3, 2001b,
pp. 387-394.
[10] S.-M. Chen, "Forecasting enrollments based on high-order fuzzy time
series," Cybernetics and Systems: An International Journal, vol. 33,
2002, pp. 1-16.
[11] S.-M. Chen, and J.-R. Hwang, "Temperature prediction using fuzzy
time series, IEEE Transactions on Systems, Man, and Cybernetics ÔÇö
Part B," Cybernetics, vol. 30, 2000, pp. 263-275.
[12] R. -C. Tsaur, J. -C. O. Yang, and H. -F. Wang, "Fuzzy relation analysis
in fuzzy time series model," Computers and Mathematics with
Applications, vol. 49, 2005, pp. 539-548.
[13] H. Li, and R. Kozma, "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, 2003, pp.
347-352.
[14] S. F. Su, and S. H. Li, "Neural network based fusion of global and local
information in predicting time series," In proceedings of the 2003 IEEE
International Joint Conference on Systems, Man and Cybernetics, vol. 5,
2003, pp. 4445-4450.
[15] J. Sullivan, and W. H. Woodall, "A comparison of fuzzy forecasting and
Markov modeling," Fuzzy Sets and Systems, vol. 64, 1996, pp. 279-
293.
[16] L. -W. Lee, L. -W. Wang, S. -M. Chen, and Y.-H. Leu, "Handling
forecasting problems based on two-factor high-order time series," IEEE
Transactions on Fuzzy Systems, vol. 14, issue 3, 2006, pp. 468-477.
[17] T. A. Jilani, S. M. A. Burney, and C. Ardil, "Multivariate high order
fuzzy time series forecasting for car road accidents," International
Journal of Computational Intelligence, vol. 4, issue 1, 2007b, pp. 15-20.
[18] T. A. Jilani, and 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, 2007. Forthcoming in Book
series Advances in Soft Computing, Springer-Verlag, 2007a.
[19] S. -T. Li, and Y. -C. Cheng, "Deterministic fuzzy time series model for
forecasting enrollments," Computers and Mathematics with
Applications, vol. 53, 2007, pp. 1904-1920.
[20] L. A. Zadeh, "Fuzzy sets," Information and Control, vol. 8, 1996, pp.
338-353.
[21] R. Koenker, "Quantile Regression", Cambridge University Press, NY-
2005.
@article{"International Journal of Information, Control and Computer Sciences:63216", author = "Tahseen A. Jilani and Aqil S. Burney and C. Ardil", title = "A New Quantile Based Fuzzy Time Series Forecasting Model", abstract = "Time series models have been used to make predictions of academic enrollments, weather, road accident, casualties and stock prices, etc. Based on the concepts of quartile regression models, we have developed a simple time variant quantile based fuzzy time series forecasting method. The proposed method bases the forecast using prediction of future trend of the data. In place of actual quantiles of the data at each point, we have converted the statistical concept into fuzzy concept by using fuzzy quantiles using fuzzy membership function ensemble. We have given a fuzzy metric to use the trend forecast and calculate the future value. The proposed model is applied for TAIFEX forecasting. It is shown that proposed method work best as compared to other models when compared with respect to model complexity and forecasting accuracy.
", keywords = "Quantile Regression, Fuzzy time series, fuzzy logicalrelationship groups, heuristic trend prediction.", volume = "2", number = "3", pages = "928-7", }
