An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Abstract

AbstractThe carbon trading market has become a powerful weapon in alleviating carbon emissions in China, and the carbon price is at the core of its operation. Hence, the carbon trading market serves as an indispensable component in forecasting the carbon price accurately in advance. This paper innovatively explores an ensemble‐driven long short‐term memory network (LSTM) model based on complementary ensemble empirical mode decomposition (CEEMD) for carbon price forecasting, applying it to all eight carbon trading pilots in China. The CEEMD was initially implemented for mode transformation in order to decompose the original complicated mode into a set of simple modes. Then, the partial autocorrelation function selected time‐lagged features as inputs for each mode. Subsequently, the LSTM was used to model the mapping between time‐lagged factors as well as each mode's target values, constructing multiple LSTM models for ensemble learning. Finally, the inverse CEEMD computation was introduced to integrate the anticipated results of the multi‐mode into the final results. Its practical application simultaneously embraced all eight carbon pilots in China, covering their corresponding carbon price data over a considerably long period. The obtained results illustrated that the proposed model driven by ensemble learning possessed sufficient accuracy in carbon price forecasting in China compared with the single LSTM model as well as other conventional artificial neural network models. Furthermore, according to the scope of its application, the innovative model exhibited strong stability and universality.