A multiple feature fusion-based intelligent optimization ensemble model for carbon price forecasting

Abstract

Accurately predicting carbon emissions trading price can assist governments in devising policies that strike a balance between economic development and environmental protection. In recent years, the decomposition integration framework has been widely applied for carbon price prediction. However, due to the inadequacy of feature extraction and lack of emphasis on interval forecasting, constructing a stable and efficient forecasting model for carbon price remains a challenging task. Thus, this study proposes an enhanced decomposition integration model for carbon price prediction that conducts multiple feature fusion based on the potential determinants and establishes a multivariate intelligent optimized ensemble model. Firstly, decomposition and entropy algorithms are employed to decompose and reconstruct the carbon price into several simple subsequences to strike the balance between computing speed and the complexity of the model. Subsequently, considering how external factors may affect the sequence, structured data, and unstructured data are both introduced to construct the multiple forecasting model. An intelligent optimization ensemble model is then developed for predicting the reconstructed components based on a deep learning model with an attention mechanism. Lastly, to quantify uncertainty, interval prediction is incorporated for research and analysis. Empirical analysis is conducted on three carbon finance pilot markets in China and the values of Mean Absolute Percentage Error (MAPE) are 2.047, 1.251, and 0.994 respectively. Furthermore, each module of the proposed model significantly contributes to the results and the developed model outperforms the baseline model across various error metrics.