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An efficient forecasting model based on an improved fuzzy time series and a modified group search optimizer

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Abstract

This paper presents a prediction model based on an improved fuzzy time series (IFTS) and a modified group search optimizer to effectively solve forecasting problems. IFTS can accurately predict whether subsequent predicted data will increase or decrease according to ratio value in the fuzzy logical relationship. In addition, the modified group search optimizer is used to adjust the length of an interval. The proposed prediction model is also used to forecast the enrollments of the University of Alabama the enrollments of a university of technology in central Taiwan, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) Experimental results show that the proposed model obtains the smallest prediction error than those of other methods.

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Authors and Affiliations

  1. Department of International Business, National Taichung University of Science and Technology, Taichung City, 404, Taiwan

    Chin-Ling Lee

  2. Department of Multimedia Animation and Application, Nan-Kai University of Technology, Nantou County, 542, Taiwan

    Shye-Chorng Kuo

  3. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City, 411, Taiwan

    Cheng-Jian Lin

Authors
  1. Chin-Ling Lee
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  2. Shye-Chorng Kuo
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  3. Cheng-Jian Lin
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Correspondence to Cheng-Jian Lin.

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Lee, CL., Kuo, SC. & Lin, CJ. An efficient forecasting model based on an improved fuzzy time series and a modified group search optimizer. Appl Intell 46, 641–651 (2017). https://doi.org/10.1007/s10489-016-0857-0

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  • DOI: https://doi.org/10.1007/s10489-016-0857-0

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