基于EMD-XGB-ELM和FSGM双元处理的碳排放交易价格集成预测

Integrated Carbon Emission Trading Price Prediction Based on EMD-XGB-ELM and FSGM from the Perspective of Dual Processing

碳排放交易价格的精准预测是推动碳金融市场可持续发展的基础。针对碳金融市场交易价格的连贯演进和同比演进特征,从纵向维度和横向维度的双元处理视角出发,以经验模态分解(EMD)、极端梯度提升(XGBoost)、极限学习机(ELM)、分数阶季节性灰色系统(FSGM)及其组合模型,构建基于随机森林(RF)的集成预测模型。选取国内外6个碳交易市场作为研究对象,并以深圳碳交所为例进行专项分析。为验证所提出模型的有效性,进一步将其与季节性指数平滑(HW)、支持向量机(SVM)、长短期记忆网络(LSTM)、FSGM、RF以及EDM-XGB-ELM等模型的预测结果进行对比。研究结果表明,本文提出的预测模型较基准模型具备更高的预测性能,且这种基于双元处理层面的建模范式在其他领域也具备较好的应用前景。

Accurate prediction of carbon emissions trading prices is the basis for stabilizing the sustainable development of the carbon financial market.When analyzing carbon emission price forecasts,existing studies often use a single modeling approach to construct forecasting models,ignoring the characteristics of changes in carbon emission prices in different dimensions.Based on the coherent and year-on-year evolution characteristics of time series,these two traditional“unit processing”modeling ideas are defined as vertical processing and horizontal processing for the first time.At the same time,these two“unit processing”modes are combined into a“dual processing”mode through the idea of integration.Then an ensemble learning model is built based on the using of Empirical Mode Decomposition(EMD),eXtreme Gradient Boosting(XGBoost),Extreme learning machine(ELM),Fractional Accumulation Order Seasonal Grey Model(FSGM),Random Forest(RF)and their combinations Model.Then,6 domestic and foreign carbon trading markets are selected as research objects,and Shenzhen Carbon Exchange is taken as an example to conduct special analysis.To verify the effectiveness of the proposed model,the prediction results of this model are further compared with Holt Winters(HW),Support Vector Machine(SVM),Long Short-Term Memory Network(LSTM),FSGM,RF and EDM-XGB-ELM.The research results show that the prediction model proposed in this paper has better prediction performance than the benchmark model,and this modeling paradigm based on dual processing level also has good application prospects in other fields.