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Improving the ANFIS Forecating Model for Time Series Based on the Fuzzy Cluster Analysis Algorithm

Author

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  • Dinh Toan Pham

    (Faculty of Mechanical - Electrical and Computer Engineering, School of Engineering and Technology, Van Lang University, Ho Chi Minh City, Vietnam)

  • Dan Nguyenthihong

    (College of Natural Sciences, Can Tho University, Can Tho City, Vietnam)

  • Tai Vovan

    (College of Natural Sciences, Can Tho University, Can Tho City, Vietnam)

Abstract

This paper proposes the forecasting model for the time series based on the improvement of the adaptive neuro-fuzzy inference system (ANFIS) method and the fuzzy cluster analysis (FCA) algorithm. In this model, (i) the authors firstly find the appropriate number of groups for the series. Then, (ii) this study determines the specific elements for each group based on the established fuzzy relationship. Finally, using the results of (i) and (ii) as the input variables, the authors improve the iterations of ANFIS method. Combining the above improvements, the efficient forecasting model for time series is proposed. The proposed model is illustrated step by step through a numerical example, and implemented rapidly by the established Matlab procedure. The experiment obtained from this model shows the outstanding advantages in comparison with the existing ones. This research can be applied well to forecast for many fields in reality.

Suggested Citation

  • Dinh Toan Pham & Dan Nguyenthihong & Tai Vovan, 2022. "Improving the ANFIS Forecating Model for Time Series Based on the Fuzzy Cluster Analysis Algorithm," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 11(1), pages 1-20, January.
    • Handle: RePEc:igg:jfsa00:v:11:y:2022:i:1:p:1-20
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