Carbon price prediction based on modified wavelet least square support vector machine

It is widely believed that setting a sensible carbon price can contribute to the mitigation of global warming, so it is particularly major to raise the precision of carbon price prediction. As such it has important implications not only for beautifying the environment but also for promoting the benign development of the carbon trading market in China. However, consideration is given to the high non-determinacy and non-linearity of the carbon price series, a single model cannot meet the prediction accuracy anymore. Since this is the case, this paper puts forward a novel hybrid forecasting model, consisting of the ensemble empirical mode decomposition (EEMD), the linearly decreasing weight particle swarm optimization (LDWPSO), and the wavelet least square support vector machine (wLSSVM). Innovatively, wLSSVM is utilized in the field of carbon price prediction for the first time. Firstly, EEMD decomposes the raw carbon price into several stable sub-sequences and a residual. Then, the inputs of each sequence are determined by the partial auto-correlation function (PACF). Next, wLSSVM optimized by LDWPSO forecasts each sequence separately. Finally, the final prediction result is obtained by adding all prediction results. For the purpose of verifying the effectiveness and superiority of the EEMD-LDWPSO-wLSSVM model, a total of 12 models were built to compare their performance in three regions of Guangdong, Hubei, and Shanghai respectively from three evaluating indicators: MAPE, RMSE, and R2. All the predicted results showed that the model presented in this paper has the best forecasting performance among all the model combinations and can substantially improve the accuracy of carbon price prediction. Therefore, the model would be an increasingly extensive application in the field of carbon price prediction in the future.