A hybrid forecasting model for enrollments based on aggregated fuzzy time series and particle swarm optimization

  • Authors:

  • Yao-Lin Huang;Shi-Jinn Horng;Mingxing He;Pingzhi Fan;Tzong-Wann Kao;Muhammad Khurram Khan;Jui-Lin Lai;I-Hong Kuo

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan;Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan and School of Mathematics and Computer Engineering, Xihua Unive ...;School of Mathematics and Computer Engineering, Xihua University, 610039 Chengdu, Sichuan, PR China;Institute of Mobile Communications Southwest Jiaotong University, 610031 Chengdu, Sichuan, PR China;Department of Electronic Engineering, Technology and Science Institute of Northern Taiwan, Taipei 112, Taiwan;Center of Excellence in Information Assurance, King Saud University, Saudi Arabia;Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;Department of Information Management, St. Mary's Medicine, Nursing and Management College, Yi-Lan 266, Taiwan

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

In this paper, a new forecasting model based on two computational methods, fuzzy time series and particle swarm optimization, is presented for academic enrollments. Most of fuzzy time series forecasting methods are based on modeling the global nature of the series behavior in the past data. To improve forecasting accuracy of fuzzy time series, the global information of fuzzy logical relationships is aggregated with the local information of latest fuzzy fluctuation to find the forecasting value in fuzzy time series. After that, a new forecasting model based on fuzzy time series and particle swarm optimization is developed to adjust the lengths of intervals in the universe of discourse. From the empirical study of forecasting enrollments of students of the University of Alabama, the experimental results show that the proposed model gets lower forecasting errors than those of other existing models including both training and testing phases.