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基于二次分解聚合策略的我国碳交易价格预测

张大斌1,2, 张博婷1, 凌立文1,2, 曾莉玲1   

  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.

张大斌, 张博婷, 凌立文, 曾莉玲. 基于二次分解聚合策略的我国碳交易价格预测[J]. 系统科学与数学, 2022, 42(11): 3094-3106.

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
碳排放权交易市场是实现碳达峰与碳中和目标的核心政策工具,为充分提取其价格序列 非线性, 非平稳性等复杂特征,文章构建一种基于二次分解聚合策略的碳交易价格预测模型CEEMD-EWT-SM-ELM.首先通过互补集合经验模态分解(CEEMD)将原始碳价格序列分解为不同频率的本征模态函数(IMF);然后利用经验小波变换(EWT)针对IMF1分量进行二次分解;再引入样本熵和最大信息系数(SE-MIC,SM)对所有分解得到的分量进行复杂度和相 关度分析,将其划分为高复杂度分量集和低复杂度分量集;进一步将低复杂度分量集聚合为一个分量,并利用极限学习机(ELM)模型对每条分量进行多步预测;最后将所有分量预测结果线性集成.文章选取广东,湖北和天津三个碳交易市场的价格数据进行实例 验证,实证结果表明:在提前1天, 3天和6天的预测中,文章提出组合模型预测精度明显优于基准模型;同时也证实了二次分解聚合策略能够有效提高碳交易价格的预测效果.
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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