Abstract
A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.
- C. H. Aladag, M. A. Basaran, E. Egrioglu, U. Yolcu, and V. R. Uslu, "Forecasting in high order fuzzy time series by using neural networks to define fuzzy relations," Expert Syst. Appl., vol. 36, no. 3, pp. 4228-4231, Apr. 2009. DOI: 10.1016/j.eswa.2008.04.001. Google ScholarDigital Library
- G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day, 1970. Google ScholarDigital Library
- S. M. Chen, "Forecasting enrollments based on fuzzy time series," Fuzzy Sets Syst., vol. 81, no. 3, pp. 311-319, Aug. 1996. Google ScholarDigital Library
- S. M. Chen, "Forecasting enrollments based on high-order fuzzy time series," Cybern. Syst., vol. 33, no. 1, pp. 1-16, Jan. 2002.Google ScholarCross Ref
- S. M. Chen and N. Y. Chung, "Forecasting enrollments using high-order fuzzy time series and genetic algorithms," Int. J. Intell. Syst., vol. 21, no. 5, pp. 485-501, May 2006. Google ScholarDigital Library
- S. M. Chen and N. Y. Chung, "Forecasting enrollments of students by using fuzzy time series and genetic algorithms," Inf. Manag. Sci., vol. 17, no. 3, pp. 1-17, 2006.Google Scholar
- S. M. Chen and C. C. Hsu, "A new method to forecast enrollments using fuzzy time series," Int. J. Appl. Sci. Eng., vol. 3, pp. 234-244, 2004.Google Scholar
- S. M. Chen and J. R. Hwang, "Temperature prediction using fuzzy time series," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 30, no. 2, pp. 263-275, Apr. 2000. Google ScholarDigital Library
- C. H. Cheng, R. J. Chang, and C. A. Yeh, "Entropy-based and trapezoidal fuzzification-based fuzzy time series approach for forecasting IT project cost," Technol. Forecast. Social Change, vol. 73, no. 5, pp. 524-542, Jun. 2006.Google ScholarCross Ref
- K. H. Huarng, "Heuristic models of fuzzy time series for forecasting," Fuzzy Sets Syst., vol. 123, no. 3, pp. 369-386, Nov. 2001.Google ScholarCross Ref
- K. H. Huarng, "Effective lengths of intervals to improve forecasting in fuzzy time series," Fuzzy Sets Syst., vol. 123, no. 3, pp. 387-394, Nov. 2001.Google ScholarCross Ref
- K. H. Huarng and T. H. K. Yu, "A multivariate heuristic model for fuzzy time-series forecasting," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 4, pp. 836-846, Aug. 2007. Google ScholarDigital Library
- K. H. Huarng and T. H. K. Yu, "Ratio-based lengths of intervals to improve fuzzy time series forecasting," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 36, no. 2, pp. 328-340, Apr. 2006. Google ScholarDigital Library
- J. R. Hwang, S. M. Chen, and C. H. Lee, "Handing forecasting problems using fuzzy time series," Fuzzy Sets Syst., vol. 100, no. 1-3, pp. 217-228, Nov. 1998. Google ScholarDigital Library
- T. A. Jilani and S. M. A. Burney, "A refined fuzzy time series model for stock market forecasting," Physica A, vol. 387, no. 12, pp. 2857-2862, May 2008.Google ScholarCross Ref
- T. A. Jilani and S. M. A. Burney, "Multivariate stochastic fuzzy forecasting models," Expert Syst. Appl., vol. 35, no. 3, pp. 691-700, Oct. 2008. Google ScholarDigital Library
- I. H. Kuo, S. J. Horng, T. W. Kao, T. L. Lin, C. L. Lee, and Y. Pan, "An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization," Expert Syst. Appl., vol. 36, no. 3, pp. 6108-6117, Apr. 2008. DOI: 10.1016/j.eswa.2008.07.043. Google ScholarDigital Library
- L. W. Lee, L. H. Wang, S. M. Chen, and Y. H. Leu, "Handling forecasting problems based on two-factors high-order fuzzy time series," IEEE Trans. Fuzzy Syst., vol. 14, no. 3, pp. 468-477, Jun. 2006. Google ScholarDigital Library
- S. T. Li, Y. C. Cheng, and S. Y. Lin, "A FCM-based deterministic forecasting model for fuzzy time series," Comput. Math. Appl., vol. 56, no. 12, pp. 3052-3063, Dec. 2008. DOI: 10.1016/j.camwa.2008.07.033. Google ScholarDigital Library
- S. R. Singh, "A simple method of forecasting based on fuzzy time series," Appl. Math. Comput., vol. 186, no. 1, pp. 330-339, Mar. 2007.Google ScholarCross Ref
- S. R. Singh, "A robust method of forecasting based on fuzzy time series," Appl. Math. Comput., vol. 188, no. 1, pp. 472-484, May 2007.Google ScholarCross Ref
- S. R. Singh, "A computational method of forecasting based on fuzzy time series," Math. Comput. Simul., vol. 79, no. 3, pp. 539-554, Dec. 2008. DOI: 10.1016/j.matcom.2008.02.026. Google ScholarDigital Library
- Q. Song and B. S. Chissom, "Fuzzy time series and its models," Fuzzy Sets Syst., vol. 54, no. 3, pp. 269-277, Mar. 1993. Google ScholarDigital Library
- Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time series--Part 1," Fuzzy Sets Syst., vol. 54, no. 1, pp. 1-9, Feb. 1993. Google ScholarDigital Library
- Q. Song and B. S. Chissom, "Forecasting enrollments with fuzzy time series--Part 2," Fuzzy Sets Syst., vol. 62, no. 1, pp. 1-8, Feb. 1994. Google ScholarDigital Library
- R. C. Tsaur, J. C. O. Yang, and H. F. Wang, "Fuzzy relation analysis in fuzzy time series model," Comput. Math. Appl., vol. 49, no. 4, pp. 539- 548, Feb. 2005. Google ScholarDigital Library
- H. Theil, Applied Economic Forecasting. New York: Rand McNally, 1996.Google Scholar
- N. Wagner, Z. Michalewicz, M. Khouja, and R. R. McGregor, "Time series forecasting for dynamic environments: The DyFor genetic program model," IEEE Trans. Evol. Comput., vol. 11, no. 4, pp. 433-452, Aug. 2007. Google ScholarDigital Library
- H. K. Yu, "A refined fuzzy time series model for forecasting," Physica A, vol. 346, no. 3/4, pp. 657-681, Feb. 2005.Google ScholarCross Ref
- H. K. Yu, "Weighted fuzzy time series model for TAIEX forecasting," Physica A, vol. 349, no. 3/4, pp. 609-624, Feb. 2005.Google ScholarCross Ref
- L. A. Zadeh, "Fuzzy sets," Inf. Control, vol. 8, no. 3, pp. 338-353, Jun. 1965.Google ScholarCross Ref
Index Terms
- Adaptive time-variant models for fuzzy-time-series forecasting
Recommendations
A heuristic time-invariant model for fuzzy time series forecasting
Many forecasting models based on the concepts of fuzzy time series have been proposed in the past decades. These models have been applied to predict enrollments, temperature, crop production and stock index, etc. In this paper, we present a simple ...
An integrated fuzzy time series forecasting system
A number of fuzzy time series models have been designed and developed during the last decade. One problem of these models is that they only provide a single-point forecasted value just like the output of the crisp time series methods. In addition, these ...
Fuzzy-time-series network used to forecast linear and nonlinear time series
Non-probabilistic forecasting methods are commonly used in various scientific fields. Fuzzy-time-series methods are well-known non-probabilistic and nonlinear forecasting methods. Although these methods can produce accurate forecasts, linear ...
Comments