An enhanced decomposition integration model for deterministic and probabilistic carbon price prediction based on two-stage feature extraction and intelligent weight optimization

Abstract

Accurately predicting carbon price can promote the rational allocation of carbon resources, reducing carbon emissions and contributing to green development. The decomposition integration framework is widely applied for carbon price forecasting, but several problems remain. Thus, this paper innovatively proposes an enhanced decomposition integration model for deterministic and probabilistic carbon price forecasting, which combines two-stage feature extraction and intelligent weight optimization. In order to decrease the complexity of the data and fully extract the characteristics, two decomposition approaches are utilized. For reconstruction, fuzzy entropy is applied to balance model complexity and computing efficiency. Next, the combination forecasting model based on two deep learning models is built to circumvent the constraints of the single forecasting model, and the cuckoo search optimization method (CS) is utilized to find the weight of the combination model adaptively. Considering the differences in the contributions of each component, the optimal weighting strategy based on CS is then used to derive the final forecast. Ultimately, considering the probability interval can provide more helpful information, the decomposition integration framework is supplemented with Gaussian process regression relying on the mixed kernel function for interval prediction. In an empirical study of five carbon trading markets, the hybrid model developed in this paper exceeded all baseline models regarding prediction stability, proving that the developed model can make effective forecasting for the carbon trading business.