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Polynomial Fuzzy Information Granule-Based Time Series Prediction

Author

Listed:
  • Xiyang Yang

    (Key Laboratory of Intelligent Computing and Information Processing, Quanzhou Normal University, Quanzhou 362000, China
    Fujian Key Laboratory of Financial Information Processing, Putian University, Putian 351100, China
    School of Mathematical Science, Beijing Normal University, Beijing 100875, China
    Fujian Provincial Key Laboratory of Data-Intensive Computing, Quanzhou Normal University, Quanzhou 362000, China)

  • Shiqing Zhang

    (Key Laboratory of Intelligent Computing and Information Processing, Quanzhou Normal University, Quanzhou 362000, China
    Fujian Provincial Key Laboratory of Data-Intensive Computing, Quanzhou Normal University, Quanzhou 362000, China)

  • Xinjun Zhang

    (Fujian Key Laboratory of Financial Information Processing, Putian University, Putian 351100, China)

  • Fusheng Yu

    (School of Mathematical Science, Beijing Normal University, Beijing 100875, China)

Abstract

Fuzzy information granulation transfers the time series analysis from the numerical platform to the granular platform, which enables us to study the time series at a different granularity. In previous studies, each fuzzy information granule in a granular time series can reflect the average, range, and linear trend characteristics of the data in the corresponding time window. In order to get a more general information granule, this paper proposes polynomial fuzzy information granules, each of which can reflect both the linear trend and the nonlinear trend of the data in a time window. The distance metric of the proposed information granules is given theoretically. After studying the distance measure of the polynomial fuzzy information granule and its geometric interpretation, we design a time series prediction method based on the polynomial fuzzy information granules and fuzzy inference system. The experimental results show that the proposed prediction method can achieve a good long-term prediction.

Suggested Citation

  • Xiyang Yang & Shiqing Zhang & Xinjun Zhang & Fusheng Yu, 2022. "Polynomial Fuzzy Information Granule-Based Time Series Prediction," Mathematics, MDPI, vol. 10(23), pages 1-21, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4495-:d:987055
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    References listed on IDEAS

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    4. Linjie He & Yumin Chen & Caiming Zhong & Keshou Wu, 2022. "Granular Elastic Network Regression with Stochastic Gradient Descent," Mathematics, MDPI, vol. 10(15), pages 1-15, July.
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