A novel framework for carbon price prediction using comprehensive feature screening, bidirectional gate recurrent unit and Gaussian process regression

Abstract

Effective analysis and prediction of carbon price can not only promote the development and maturity of carbon trading market, but also contribute to the rational allocation of carbon resources. In order to improve the prediction accuracy of carbon prices and provide more reference information for related researchers and market practitioners, this study proposes a novel carbon price forecasting model, which innovatively combines the comprehensive feature screening technology (CFS) and the method of probability estimation. First of all, the carbon price data is decomposed by the improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the wavelet transform (WT) algorithm is used to denoise after obtaining the combination of intrinsic mode function (IMF) with different complexity. Then, a feature screening technology that combines the advantages of principal component analysis (PCA), random forest (RF) and gradient boosted decision tree (GBDT) methods is design to deeply extract the influencing factors of predictive variables. Finally, as an improved predictor, bidirectional gated recurrent unit (BIGRU) is used to establish a point prediction model, and on this basis, Gaussian process regression (GPR) is used to measure the probability interval of carbon price change. In the empirical analysis of the three markets, the hybrid model proposed in this study is better than other comparison models. Taking the Shanghai market as an example, the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model are 1.554, 1.081 and 0.028, respectively. In the other two markets, the hybrid model proposed in this study also performs best.