Fuzzy Autocorrelation Model with Fuzzy Confidence Intervals and its Evaluation

• Yabuuchi Yoshiyuki

  Faculty of Economics, Shimonoseki City University

• Kawaura Takayuki

  Department of Mathematics, Kansai Medical University

• Watada Junzo

  World Collaborative Innovation Center of Management Engineering

抄録

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.

収録刊行物

• Journal of Advanced Computational Intelligence and Intelligent Informatics

  Journal of Advanced Computational Intelligence and Intelligent Informatics 20 (4), 512-520, 2016-07-20

  富士技術出版株式会社

キーワード

• fuzzy time-series model
• Box-Jenkins model
• autocorrelation
• fuzzy random variable

詳細情報

• CRID: 1390282763128048512
• NII論文ID: 130007673382
• NII書誌ID: AA12042502
• DOI: 10.20965/jaciii.2016.p0512
• ISSN: 18838014
• ISSN: 13430130
• NDL書誌ID: 027611869
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I027611869
• Web Site: https://www.fujipress.jp/main/wp-content/themes/Fujipress/phyosetsu.php?ppno=JACII002000040003
• 本文言語コード: en
• データソース種別:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
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