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Carbon price forecasting with optimization prediction method based on unstructured combination

机译：基于非结构化组合的优化预测方法碳价格预测

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摘要

The construction of carbon emission trading market is gradually improved, making carbon assets have financial nature, which can effectively restrain excessive carbon emissions. Accurate prediction of the carbon price is of great significance to the scientific decision-making of the government. In order to make the prediction more accurate and reasonable, this paper proposes a new combinatorial optimization prediction method based on unstructured data. In the model, firstly, the structured data screened by grey correlation method and factor analysis and the unstructured data screened by Baidu index are taken as one of the input ends of prediction. Secondly, the Mean value Optimization (MOEMD) method is used to decompose the fluctuating carbon price as the other part of the input of the prediction model. Then, based on the optimized Extreme Learning Machine (ELM) prediction model, the Kidney Algorithm (KA) algorithm with scaling factor and cooperation factor (CKA) model are established to predict the carbon trading price of China. Finally, simulation experiments are carried out in eight pilot areas in China to verify the effectiveness of the model. The results show that the MOEMD-CKA-ELM performs well in carbon price prediction, and the unstructured learning method effectively improves the prediction performance of the model.
机译：碳排放交易市场的建设逐渐提高，碳资产具有金融性质，可有效抑制过度碳排放量。准确预测碳价格对政府的科学决策具有重要意义。为了使预测更准确和合理，本文提出了一种基于非结构化数据的新组合优化预测方法。在该模型中，首先，通过灰色相关方法和因子分析筛选的结构化数据以及百度索引筛选的非结构化数据被视为预测的输入端之一。其次，平均值优化（MoEMD）方法用于将波动碳价格分解为预测模型的输入的另一部分。然后，基于优化的极限学习机（ELM）预测模型，建立了具有缩放因子和合作因子（CKA）模型的肾算法（KA）算法预测中国的碳交易价格。最后，仿真实验在中国的八个试点区域进行，以验证模型的有效性。结果表明，MoEmd-CKA-ELM在碳价格预测中表现良好，非结构化学习方法有效提高了模型的预测性能。

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