Carbon price forecasting based on modified ensemble empirical mode decomposition and long short-term memory optimized by improved whale optimization algorithm.

Abstract

The accurate prediction of carbon prices poses a tremendous challenge to relevant industry practitioners and governments. This paper proposes a novel hybrid model incorporating modified ensemble empirical mode decomposition (MEEMD) and long short-term memory (LSTM) optimized by the improved whale optimization algorithm (IWOA). This model is based on the nonlinear and non-stationary characteristics of carbon price. The original carbon price is first decomposed into nine intrinsic mode functions (IMFs) and a residual using the MEEMD model. Then, the random forest method is applied to determine the input variables of each IMF and the residual, in the LSTM neural network. The carbon price is then predicted by the LSTM model optimized by the IWOA. The proposed hybrid model is applied to predict the carbon prices of Beijing, Fujian, and Shanghai to assess its effectiveness. The results reveal that the model achieved higher prediction performance than 11 other benchmark models. Our observations indicate that decomposition of carbon price can effectively improve the accuracy of prediction. Moreover, the improved LSTM model is more suitable for time series prediction. The proposed model provides a novel and effective carbon price forecasting tool for governments and enterprises.