Carbon Emissions Trading Price Forecasting Based on Combined CEEMDAN-GRU Model
Accurate carbon price forecasting helps regulators to observe the operation of the carbon trading market and inves-tors to make scientific decisions,which plays an essential role in achieving the goals of carbon peaking and carbon neutrality.However,the non-linearity,non-smoothness and high noise characteristics of carbon price series make it difficult to forecast them precisely.A carbon price prediction model was constructed by combining the complete ensemble empirical mode decomposi-tion with adaptive noise(CEEMDAN)method with a gate recurrent unit(GRU).The model was developed based on the idea of decomposition and integration,using CEEMDAN to decompose the original series to obtain the intrinsic mode functions(IMFs)and residual series at different frequencies,followed by using the GRU neural network to build prediction models for each sub-series separately,and finally integrating the prediction results to obtain the forecasting carbon price.Taking the daily transaction price of the Hubei carbon trading market as an example for empirical analysis,the results show that the CEEMDAN-GRU model has smaller prediction error and higher fitting effect than the other five benchmark models,which provides certain advantages in carbon price prediction.