An accurate calculation method of carbon trading price is of great significance to strengthening energy saving and emission reduction. Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new hybrid model for carbon trading price forecasting. The model fuses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) with extreme gradient boosting (XGBoost) and long short-term memory (LSTM) networks, and leverages SnowNLP to de...
