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JACIII Vol.18 No.2 pp. 197-203
doi: 10.20965/jaciii.2014.p0197
(2014)

Paper:

Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data

Yoshiyuki Yabuuchi* and Junzo Watada**

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

**Graduate School of Information, Production and Systems, Waseda University, 2-4 Hibikino, Wakamatsu, Kitakyushu, Fukuoka 808-0196, Japan

Received:
October 14, 2013
Accepted:
January 14, 2014
Published:
March 20, 2014
Keywords:
fuzzy time-series model, possibility, autocorrelation, fuzzy random variables, confidence intervals
Abstract

Economic analyses are typical methods based on timeseries data or cross-section data. Economic systems are complex because they involve human behaviors and are affected by many factors. When a system includes such uncertainty, as those concerning human behaviors, a fuzzy system approach plays a pivotal role in such analysis. In this paper, we propose a fuzzy autocorrelation model with confidence intervals of fuzzy random timeseries data. These confidence intervals play an essential role in dealing with fuzzy random data on the fuzzy autocorrelation model that we have presented. We analyze tick-by-tick data of stock transactions and compare two time-series models, a fuzzy autocorrelation model proposed by us, and a new fuzzy time-series model that we propose in this paper.

Cite this article as:
Y. Yabuuchi and J. Watada, “Fuzzy Autocorrelation Model with Confidence Intervals of Fuzzy Random Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.2, pp. 197-203, 2014.
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Last updated on Nov. 16, 2018