Polynomial Fuzzy Information Granule-Based Time Series Prediction

Xiyang Yang, Shiqing Zhang, Xinjun Zhang and Fusheng Yu ()
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Xiyang Yang: Key Laboratory of Intelligent Computing and Information Processing, Quanzhou Normal University, Quanzhou 362000, China
Shiqing Zhang: Key Laboratory of Intelligent Computing and Information Processing, 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

Mathematics, 2022, vol. 10, issue 23, 1-21

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.

Keywords: polynomial fuzzy information granule; fuzzy inference system; time series prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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