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Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting

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Abstract

The use of fuzzy time series has attracted considerable attention in studies that aim to make forecasts using uncertain information. However, most of the related studies do not use a learning mechanism to extract valuable information from historical data. In this study, we propose an evolutionary fuzzy forecasting model, in which a learning technique for a fuzzy relation matrix is designed to fit the historical data. Taking into consideration the causal relationships among the linguistic terms that are missing in many existing fuzzy time series forecasting models, this method can naturally smooth the defuzzification process, thus obtaining better results than many other fuzzy time series forecasting models, which tend to produce stepwise outcomes. The experimental results with two real datasets and four indicators show that the proposed model achieves a significant improvement in forecasting accuracy compared to earlier models.

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Acknowledgments

The authors gratefully appreciate the financial support provided by the National Science Council, Taiwan, R.O.C., under contracts NSC 101-2410-H-434-001 and NSC99-2410-H-006-054-MY3.

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Authors and Affiliations

  1. Department of Industrial and Information Management, National Cheng Kung University, No. 1, University Road, Tainan, 701, Taiwan, ROC

    Sheng-Tun Li

  2. Institute of Information Management, National Cheng Kung University, No. 1, University Road, Tainan, 701, Taiwan, ROC

    Sheng-Tun Li

  3. Department of Leisure Information Management, Taiwan Shoufu University, No. 168, Nanshi Li, Madou District, Tainan, 721, Taiwan, ROC

    Shu-Ching Kuo & Chih-Chuan Chen

Authors
  1. Shu-Ching Kuo
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  2. Chih-Chuan Chen
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  3. Sheng-Tun Li
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Correspondence to Sheng-Tun Li.

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Kuo, SC., Chen, CC. & Li, ST. Evolutionary Fuzzy Relational Modeling for Fuzzy Time Series Forecasting. Int. J. Fuzzy Syst. 17, 444–456 (2015). https://doi.org/10.1007/s40815-015-0043-2

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  • DOI: https://doi.org/10.1007/s40815-015-0043-2

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