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### 基于二次分解聚合策略的我国碳交易价格预测

1. 1. 华南农业大学数学与信息学院, 广州 510642;
2. 华南农业大学乡村振兴研究院, 广州 510642
• 收稿日期:2022-04-20 修回日期:2022-07-04 发布日期:2022-12-13
• 通讯作者: 凌立文, Email: linglw@scau.edu.cn
• 基金资助:
国家自然科学基金(71971089, 72001083), 广东省基础与应用基础研究基金(2022A1515011612)资助课题.

ZHANG Dabin, ZHANG Boting, LING Liwen, ZENG Liling. Carbon Price Forecasting Based on Secondary Decomposition and Aggregation Strategy[J]. Journal of Systems Science and Mathematical Sciences, 2022, 42(11): 3094-3106.

### Carbon Price Forecasting Based on Secondary Decomposition and Aggregation Strategy

ZHANG Dabin1,2, ZHANG Boting1, LING Liwen1,2, ZENG Liling1

1. 1. College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642;
2. Rural Development Institute, South China Agricultural University, Guangzhou 510642
• Received:2022-04-20 Revised:2022-07-04 Published:2022-12-13

The carbon emission trading market is the core policy tool to achieve the goal of carbon peak and carbon neutrality. In order to fully extract the nonlinear and non-stationary characteristics of the price series, our paper constructs a carbon trading price prediction model, CEEMD-EWT-SM-ELM, proposed based on the secondary decomposition and aggregation strategy. Firstly, the original carbon price sequence is decomposed into intrinsic mode functions (IMF) of different frequencies by complementary set empirical mode decomposition (CEEMD). Then, the empirical wavelet transform (EWT) is used to secondarily decompose the IMF1 components. After that, sample entropy and maximum information coefficient (SE-MIC, SM) were introduced to analyze the complexity and relevance of all decomposed components, which were divided into the high complexity component set and the low complexity component set. Furthermore, the low complexity components were aggregated into one component, and the ELM model was used to predict each component. Finally, the prediction results of the reconstruction components were linearly integrated. This paper selects the price data of three carbon trading markets in Guangdong, Hubei, and Tianjin for example verification. The empirical results show that: In the prediction of one, three, and six days in advance, the prediction accuracy of the combined model is better than that of the benchmark model, which in turn proves that the secondary decomposition and aggregation strategy can effectively improve the prediction effect of carbon trading price.

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