Carbon price prediction is of cardinal significance for promoting the development of carbon trading market. However, the strong non-stationary and nonlinearity characteristics of the carbon price pose a challenge to the carbon price prediction model. To improve the accuracy of carbon price prediction, a combined model based on ensemble empirical mode decomposition (EEMD) is proposed in this paper. In the proposed model, EEMD is used to decompose the original data into a series of relatively stable component sequences. Then, the extreme learning machine (ELM) and BP neural network (BP) optimized by particle swarm optimization algorithm (PSO) are used to respectively predict the component sequences and integrate the predicted results. Finally, the weights of the two prediction methods are determined by the variance-covariance method, and the final combined prediction result is obtained. To verify the performance of proposed model, the carbon price sequences of Shenzhen and Hubei of China were selected. The results showed that the combined model had good performance.

Hongkai Zhao, Nan Ye, Bowen Xia. A Novel Hybrid Model Using EEMD and Neural Network for Forecasting Carbon Price. Academic Journal of Humanities & Social Sciences (2020) Vol. 3, Issue 5: 96-116. https://doi.org/10.25236/AJHSS.2020.030512.

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