Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks

The forecasting of carbon prices is critical to understand China unified carbon market dynamics. In this study, in order to reduce the noise and modal aliasing of carbon price sequence, a novel hybrid forecasting model is presented to predict carbon price in China unified carbon market. First, a three-stage algorithm that combines the improved complete ensemble empirical mode decomposition with adaptive noise (iCEEMDAN), variational mode decomposition (VMD), and reconstruction of fine-to-coarse (REC) data is proposed. Time series data are decomposed and reconstructed by this three-stage algorithm into three subsequences. Secondly, the model which combines the long short-term memory (LSTM) and convolutional neural networks (CNN) is utilized for prediction. Finally, the empirical results indicate that the prediction accuracy of this hybrid forecasting model is improved around 65 % higher than that of traditional LSTM. The proposed hybrid forecasting model can help the enterprises to make decisions facing non-linear, non-stationary and irregular carbon price more effectively. It is conducive to the implementation of energy conservation and emission reduction policies for the governments.