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International Conference on Knowledge-Based and Intelligent Information and Engineering Systems

KES 2004: Knowledge-Based Intelligent Information and Engineering Systems pp 151–158Cite as

Possibilistic Forecasting Model and Its Application to Analyze the Economy in Japan

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3215))

Abstract

It is hard to separate samples according to each latent system in the case of multivariate data. Hitherto, there are many researches to investigate the structure under obtained data and analyze such data. J. C. Bezdek proposes Switching Regression Model based on Fuzzy Clustering Model to formulate a forecasting model. The model proposed by Bezdek is to separate mixed samples coming from plural latent systems and apply each regression model to the group of samples coming from each system. That is a Fuzzy c-Regression Model.

In this paper, in order to deal with the possibility of a system, we employ a fuzzy forecasting model such as a Fuzzy Switching Regression Model and a Fuzzy Switching Time Series Model. The fuzzy forecasting regression model is explained to analyze the economy in Japan.

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References

  1. Hathaway, R.J., Bezdek, J.C.: Switching Regression Models and Fuzzy Clustering. IEEE Transactions on Fuzzy Systems 1(3), 195–204 (1993)

    Article  Google Scholar 

  2. Ishibuchi, H., Tanaka, H.: Interval Regression Analysis based on Mixed 0-1 Integer Programming problem. Journal of Japan Industrial Management Association 40(5), 312–319 (1988) (in Japanese)

    Google Scholar 

  3. Ozawa, K., Watanabe, T., Kanke, M.: Forecasting Fuzzy Times Series with Fuzzy AR Model. In: Proceedings of 20th International Conference on Computers & Industrial Engineering, Kyongju, Korea, pp. 105–108 (1996)

    Google Scholar 

  4. Ryoke, M., Nakamori, Y.: Simulataneous Analysis of Classification and Regression by Adaptive Fuzzy Clustering. Journal of Japan Society for Fuzzy Theory and Systems 8(1), 136–146 (1996) (in Japanese)

    Article  Google Scholar 

  5. Watada, J.: Fuzzy Time-series Analysis and Its Forecasting of Sales Volume. In: Kacprzyk, J., Fedrizzi, M. (eds.) Fuzzy Regression Analysis, pp. 211–227 (1992)

    Google Scholar 

  6. Yabuuchi, Y., Watada, J.: Fuzzy Robust Regression Analysis based on A Hyperelliptic Function. In: Proceedings of the 4th IEEE International Conference on Fuzzy Systems, pp. 1841–1848 (1995)

    Google Scholar 

  7. Yabuuchi, Y., Watada, J.: Fuzzy Switching Regression Model based on Genetic Algorithm. In: Proceedings of the 7th International Fuzzy Systems Association World Congress, Prague, Czech Republic, pp. 113–118 (1997)

    Google Scholar 

  8. Yabuuchi, Y., Toyoura, Y., Watada, J.: Fuzzy AR Model of Stock Price. In: Proceedings of 5th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty, Mt. Koya, Japan, pp. 127–132 (2002)

    Google Scholar 

  9. Yabuuchi, Y., Watada, J., Toyoura, Y.: Fuzzy Switching AR Model. In: Proceedings of the 19th Fuzzy System Symposium, pp. 697–698 (2003) (in Japanese)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Shimonoseki City University, 2-1-1 Daigaku, Shimonoseki, Yamaguchi, 751-8510, Japan

    Yoshiyuki Yabuuchi

  2. Waseda University, 2-7 Hibikino, Wakamatsuku, Kitakyushu, Fukuoka, 808-0135, Japan

    Junzo Watada

Authors
  1. Yoshiyuki Yabuuchi
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  2. Junzo Watada
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Editor information

Editors and Affiliations

  1. KES International, 2nd Floor, 145-157 St John Street, EC1V 4PY, London, United Kingdom

    Mircea Gh. Negoita

  2. Centre for SMART systems Engineering Research Centre, University of Brighton, BN2 4GJ, Moulsecoomb, Brighton, UK

    Robert J. Howlett

  3. School of Electrical and Information Engineering, Knowledge Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Lakhmi C. Jain

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© 2004 Springer-Verlag Berlin Heidelberg

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Yabuuchi, Y., Watada, J. (2004). Possibilistic Forecasting Model and Its Application to Analyze the Economy in Japan. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_22

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  • DOI: https://doi.org/10.1007/978-3-540-30134-9_22

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23205-6

  • Online ISBN: 978-3-540-30134-9

  • eBook Packages: Springer Book Archive

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