JACIII Vol.20 No.4 pp. 512-520
doi: 10.20965/jaciii.2016.p0512


Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals and its Evaluation

Yoshiyuki Yabuuchi*, Takayuki Kawaura**, and Junzo Watada***

*Faculty of Economics, Shimonoseki City University
2-1-1 Daigaku-cho, Shimonoseki, Yamaguchi 751-8510, Japan

**Department of Mathematics, Kansai Medical University
2-5-1 Shin-machi, Hirakata, Osaka 573-1010, Japan

***World Collaborative Innovation Center of Management Engineering
2-10-8-407 Kobai, Yawatanishi, Kitakyushu City, Fukuoka 806-0011, Japan

October 31, 2015
March 13, 2016
July 19, 2016
fuzzy time-series model, Box-Jenkins model, autocorrelation, fuzzy random variable
Interval models based on fuzzy regression and fuzzy time-series can illustrate the possibilities of a system using the intervals in the model. Thus, the aim is to minimize the vagueness of the model in order to describe the possible states of the system. In the present study, we consider on an interval fuzzy time-series model based on a Box–Jenkins model, a fuzzy autocorrelation model proposed by Yabuuchi, and a fuzzy regressive model proposed by Ozawa. We examine two models by analyzing the Japanese national consumer price index and demonstrate that our approach improves the accuracy of predictions. The utility and predictive accuracy of fuzzy time-series models are validated using two concepts of fuzzy theory and statistics. Finally, we demonstrate the applicability of the fuzzy autocorrelation model with fuzzy confidence intervals.
Cite this article as:
Y. Yabuuchi, T. Kawaura, and J. Watada, “Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals and its Evaluation,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.4, pp. 512-520, 2016.
Data files:
  1. [1] K. Ozawa, T. Niimura, and T. Nakahima, “Fuzzy Time-Series Model of Electric Power Consumption,” J. of Advanced Computational Intelligence, Vol.4, No.3, pp. 188-194, 2000.
  2. [2] Y. Yabuuchi and J. Watada, “Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data,” Proc. the 6th Int. Conf. on Soft Computing and Intelligent Systems, and the 13th Int. Symp. on Advanced Intelligent Systems, pp. 1938-1943, 2012.
  3. [3] Y. Yabuuchi and J. Watada, “Building Fuzzy Autocorrelation Model and Its Application to Analyzing Stock Price Time-Series Data,” in W. Pedrycz and S.-M. Chen (Eds.), Time Series Analysis, Modeling and Applications, Springer-Verlag Berlin Heidelberg, pp.347–367, 2012.
  4. [4] Y. Yabuuchi and T. Kawaura, “Analysis of Japanese National Consumer Price Index using Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals,” Proc. Int. Conf. on Advanced Mechatronic Systems, pp. 264-269, 2014.
  5. [5] Y. Yabuuchi, T. Kawaura, and J. Watada, “Fuzzy Autocorrelation Model and Its Evaluation,” Proc. the 11th Int. Symp. on Management Engineering, pp. 47-54, 2015.
  6. [6] F. M. Tseng, G. H. Tzeng, H. C. Yu, and B. J. C. Yuan, “Fuzzy ARIMA Model for forecasting the foreign exchange market,” Fuzzy Sets and Systems, Vol.118, Issue 1, pp. 9-19, 2001.
  7. [7] F. M. Tseng, and G. H. Tzeng, “A fuzzy seasonal ARIMA model for forecasting,” Fuzzy Sets and Systems, Vol.126, Issue 3, pp. 367-376, 2002.
  8. [8] J. Watada, “Possibilistic Time-series Analysis and Its Analysis of Consumption,” D. Dubois, H. Prade, and R. R. Yager (ed.), Fuzzy Information Engineering, John Wiley & Sons, INC., pp. 187-217, 1996.
  9. [9] A. Colubi, “Statistical inference about the means of fuzzy random variables: Applications to the analysis of fuzzy- and real-valued data,” Fuzzy Sets and Systems, Vol.160, Issue 3, pp. 344-356, 2009.
  10. [10] J. Chachi and S. M. Taheri, “Fuzzy confidence intervals for mean of Gaussian fuzzy random variables,” Expert Systems with Applications, Vol.38, Issue 5, pp. 5240-5244, 2011.
  11. [11] I. Couso, D. Dubois, S. Montes, and L. Sánchez, “On various definitions of the various of a fuzzy random variable,” Proc. 5th Int. Symp. on Imprecise Probabilities: Theories and Applications, pp. 135-144, 2007.
  12. [12] I. Couso and L. Sánchez, “Upper and lower probabilities induced by a fuzzy random variable,” Fuzzy Sets and Systems, Vol.165, Issue 1, pp. 1-23, 2011.
  13. [13] H. Kwakernaak, “Fuzzy random variables-I. definitions and theorems,” Information Sciences, Vol.15, Issue 1, pp. 1-29, 1978.
  14. [14] H. Kwakernaak, “Fuzzy random variables-II, Algorithms and examples for the discrete case,” Information Sciences, Vol.17, Issue 3, pp. 253-278, 1979.
  15. [15] M. L. Puri and D. A. Ralescu, D. A., “The Concept of Normality for Fuzzy Random Variables,” Ann. Probab., Vol.13, No.4, pp. 1373-1379, 1985.
  16. [16] J. Watada, S. Wang, and W. Pedrycz, “Building Confidence-Interval-Based Fuzzy Random Regression Models,” IEEE Trans. on Fuzzy Systems, Vol.17, No.6, pp. 1273-1283, 2009.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jul. 12, 2024