An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization

  • Authors:

  • I-Hong Kuo;Shi-Jinn Horng;Tzong-Wann Kao;Tsung-Lieh Lin;Cheng-Ling Lee;Yi Pan

  • Affiliations:

  • Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan and Department of Computer Science & Information Engineering, National Taiwan Univers ...;Department of Electronic Engineering, Technology and Science Institute of Northern Taiwan, Taipei, Taiwan;Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan and Department of Electronic Engineering, Technology and Science Institute of Norther ...;Department of Electro-Optical Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, United States

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

Many forecasting models based on the concept of fuzzy time series have been proposed in the past decades. Two main factors, which are the lengths of intervals and the content of forecast rules, impact the forecasted accuracy of the models. How to find the proper content of the main factors to improve the forecasted accuracy has become an interesting research topic. Some forecasting models, which combined heuristic methods or evolutionary algorithms (such as genetic algorithms and simulated annealing) with the fuzzy time series, have been proposed but their results are not satisfied. In this paper, we use the particle swarm optimization to find the proper content of the main factors. A new hybrid forecasting model which combined particle swarm optimization with fuzzy time series is proposed to improve the forecasted accuracy. The experimental results of forecasting enrollments of students of the University of Alabama show that the new model is better than any existing models, and it can get better quality solutions based on the first-order and the high-order fuzzy time series, respectively.