A multiperiod grey prediction model and its application

Article type: Research Article

Authors: Luo, D.^a | Zhang, G.Z.^{a; b; *}

Affiliations: [a] North China University of Water Resources and Electric Power, Zhengzhou, Henan, P.R. China | [b] Henan University of Economics and Law, Zhengzhou, Henan, P.R. China

Correspondence: [*] Corresponding author. G.Z. Zhang. E-mail: zhangguozheng2017@126.com.

Abstract: The purpose of this paper is to solve the prediction problem of nonlinear sequences with multiperiodic features, and a multiperiod grey prediction model based on grey theory and Fourier series is established. For nonlinear sequences with both trend and periodic features, the empirical mode decomposition method is used to decompose the sequences into several periodic terms and a trend term; then, a grey model is used to fit the trend term, and the Fourier series method is used to fit the periodic terms. Finally, the optimization parameters of the model are solved with the objective of obtaining a minimum mean square error. The novel model is applied to research on the loss rate of agricultural droughts in Henan Province. The average absolute error and root mean square error of the empirical analysis are 0.3960 and 0.5086, respectively. The predicted results show that the novel model can effectively fit the loss rate sequence. Compared with other models, the novel model has higher prediction accuracy and is suitable for the prediction of multiperiod sequences.

Keywords: Nonlinear sequences, multiperiod, grey model, empirical mode decomposition, Fourier series

DOI: 10.3233/JIFS-202775

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 6, pp. 11577-11586, 2021