An improved method for forecasting enrollments based on fuzzy time series and particle swarm optimization |
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| Authors: | I-Hong Kuo Shi-Jinn Horng Tzong-Wann Kao Tsung-Lieh Lin Cheng-Ling Lee Yi Pan |
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| Affiliation: | 1. Department of Electrical Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan;2. Department of Computer Science & Information Engineering, National Taiwan University of Science and Technology, 106 Taipei, Taiwan;3. Department of Electronic Engineering, National United University, 36003 Miao-Li, Taiwan;4. Department of Electronic Engineering, Technology and Science Institute of Northern Taiwan, Taipei, Taiwan;5. Department of Electro-Optical Engineering, National United University, 36003 Miao-Li, Taiwan;6. Department of Computer Science, Georgia State University, Atlanta, GA 30302-4110, United States;1. School of Control Science and Engineering, Dalian University of Technology, Dalian City, PR China;2. Department of Electrical Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada;3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Computer Science & Engineering Department, Bhagwan Parshuram Institute of Technology, Delhi, India;2. Computer Science & Engineering Department, Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India;3. Computer Science & Eng. Department, Indira Gandhi Delhi Technological University for Women, Kashmere Gate, New Delhi, India;4. Tijuana Institute of Technology, Calzada Tecnologico s/n, CP 22379, Tijuana, Mexico;1. Research Center of Information and Control, Dalian University of Technology, Dalian City, 116024, PR China;2. Department of Mathematics, Jilin Institute of Chemical Technology, Jilin 132022, China;3. Department of Electrical & Computer Engineering, University of Alberta, Edmonton Canada;1. Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China;2. School of Mathematics and System Science, Shenyang Normal University, Shenyang 110034, China;3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada T6R 2G7 |
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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. |
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