Forecasting enrollments using high-order fuzzy time series and genetic algorithms: Research Articles

  • Authors:

  • Shyi-Ming Chen;Nien-Yi Chung

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, R.O.C.

  • Venue:
  • International Journal of Intelligent Systems
  • Year:
  • 2006

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Abstract

In recent years, many researchers have presented different forecasting methods to deal with forecasting problems based on fuzzy time series. When we deal with forecasting problems using fuzzy time series, it is important to decide the length of each interval in the universe of discourse due to the fact that it will affect the forecasting accuracy rate. In this article, we present a new method to deal with the forecasting problems based on high-order fuzzy time series and genetic algorithms, where the length of each interval in the universe of discourse is tuned by using genetic algorithms, and the historical enrollments of the University of Alabama are used to illustrate the forecasting process of the proposed method. The proposed method can achieve a higher forecasting accuracy rate than the existing methods. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 485–501, 2006.