基于GMDH-PSO-LSSVM的国际碳市场价格预测

Carbon price prediction based on integration of GMDH,particle swarm optimization and least squares support vector machines

针对国际碳市场价格预测LSSVM建模输入节点和模型参数难以确定的问题,建立了基于数据分组处理方法(GMDH)-粒子群算法(PSO)-最小二乘支持向量机(LSSVM)的国际碳市场价格预测模型.首先利用GMDH算法获得LSSVM建模中的输入变量;其次应用PSO算法对LSSVM建模中的参数进行优化,进而使用训练好的LSSVM模型对测试样本进行预测;最后采用该模型对欧盟排放交易体系(EU ETS)两个不同到期时间的碳期货价格(DEC 10和DEC 12)进行实证分析,取得了令人满意的效果.

Aiming at the problems of determining the inputs and parameters for least squares support vector machines （LSSVM） modeling, this paper presents an integrated model of group method of data handling （GMDH）, particle swarm optimization （PSO） and LSSVM, i.e., GMDH-PSO-LSSVM, for international carbon price prediction. First, GMDH is used to make the selection of input-layer units easily. Next, PSO is used to train LSSVM model with the training samples and obtain the optimal parameters. Then, the trained LSSVM is used to forecast carbon price of the testing samples. Finally, taking two carbon futures prices with different maturity called DEC 10 and DEC 12 of European Union emissions trading scheme （EU ETS） as samples, empirical results show that the proposed model is an effective way to improve forecasting accuracy.