Polynomial Fuzzy Information Granule-Based Time Series Prediction

• Published in: Mathematics (Basel) Vol. 10; no. 23; p. 4495
• Main Authors: Yang, Xiyang, Zhang, Shiqing, Zhang, Xinjun, Yu, Fusheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2022
• Subjects: Analysis; Artificial intelligence; Fuzzy algorithms; fuzzy inference system; Fuzzy logic; Fuzzy sets; Fuzzy systems; Granular materials; Granulation; Neural networks; polynomial fuzzy information granule; Polynomials; Prediction theory; Time series; time series prediction; Time-series analysis; Trends; Windows (intervals); China
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10234495

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.