Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Jianguo Zhou, Xuejing Huo, Xiaolei Xu and Yushuo Li
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Jianguo Zhou: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China
Xuejing Huo: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China
Xiaolei Xu: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China
Yushuo Li: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071003, China

Energies, 2019, vol. 12, issue 5, 1-22

Abstract: Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

Keywords: extreme-point symmetric mode decomposition; extreme learning machine; grey wolf optimizer algorithm; carbon price forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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