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

Paper:

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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

Received:
October 31, 2015
Accepted:
March 13, 2016
Published:
July 19, 2016
Creative Commons License
Keywords:
fuzzy time-series model, Box-Jenkins model, autocorrelation, fuzzy random variable
Abstract
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.
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