A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Jianguo Zhou and Shiguo Wang
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Jianguo Zhou: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China
Shiguo Wang: Department of Economics and Management, North China Electric Power University, 689 Huadian Road, Baoding 071000, China

Energies, 2021, vol. 14, issue 5, 1-20

Abstract: Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

Keywords: carbon price; empirical mode decomposition; variational mode decomposition; sparrow search algorithm; kernel extreme learning machine; secondary decomposition; partial autocorrelation analysis; maximum correlation minimum redundancy algorithm (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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