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Fuzzy Regression For Seasonal Time Series Analysis

Author

Listed:
  • RUEY-CHYN TSAUR

    (Department of Finance, Hsuan Chuang University, Hsinchu, Taiwan, ROC)

  • HSIAO-FAN WANG

    (Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, 30043, Taiwan, ROC)

  • JIA-CHI O.-YANG

    (Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, 30043, Taiwan, ROC)

Abstract

Fuzzy regression model is an alternative to evaluate the relation between independent variables and dependent variable among the forecasting models when the data are not sufficient to identify the relation. Such phenomenon is significant especially for seasonal variation data for which large amount of data are required to show the pattern. However, few researches have been done on this issue. Because of its increasing importance in industries, in this study, we propose a method of applying fuzzy regression model for this purpose. By using two independent variables of preceding periodical data and index of time, the developed model not only shows the pattern of the seasonal variation, but also the yearly trend. From the results of the illustration, the average forecasting error is below 1.85% which, in comparison to the most commonly used Quadratic Trend Analysis of 2.91% and the Double Exponential Smoothing Model of 4.29%, has a better performance.

Suggested Citation

  • Ruey-Chyn Tsaur & Hsiao-Fan Wang & Jia-Chi O.-Yang, 2002. "Fuzzy Regression For Seasonal Time Series Analysis," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 1(01), pages 165-175.
    • Handle: RePEc:wsi:ijitdm:v:01:y:2002:i:01:n:s0219622002000117
    DOI: 10.1142/S0219622002000117
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    Cited by:

    1. Brad S. Trinkle, 2005. "Forecasting annual excess stock returns via an adaptive network‐based fuzzy inference system," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 13(3), pages 165-177, July.

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