为提高碳市场价格预测的准确性,提出了一种基于相空间重构(PSR)和最小二乘支持向量回归(LSSVR)模型参数同步优化的碳市场价格预测模型(PSO-PSR-LSSVR).该模型基于碳市场价格数据特征,利用PSO算法自适应同步优化PSR和LSSVR参数,有效克服了模型参数单独优化和轮流优化的缺陷,保证了参数组合的整体最优.以欧盟碳排放交易体系(EU ETS)下两个碳期货价格为研究对象,实证结果表明,相比常用的预测方法,该模型能够获得更高的预测精度.

Aiming at enhancing the accuracy of carbon price forecasting, a novel model of carbon prices forecasting based on simultaneous optimization for phase space reconstruction (PSR) and least square support vector regression (LSSVR) using particle swarm optimization (PSO) is proposed. The optimal parameters are obtained simultaneously by using the PSO algorithm on the basis of data characteristics, which can overcome the drawbacks of separate optimization and alternative optimization. Finally, taking two carbon future prices under the European Union emissions trading scheme (EU ETS) as samples, the empirical results show that, compared with the traditional forecasting approaches, the proposed model has a better forecasting accuracy.