A novel interval-valued carbon price forecasting paradigm: multi-factor intelligent recognition-based ensemble learning

Accurate carbon price prediction is of significant importance for the sustainable growth of the carbon market, as it influences the realization of dual carbon goals and the transition toward a low-carbon economy. However, previous forecasting models have typically been point-valued carbon price-based, and the factors influencing carbon prices have not been comprehensively identified. Therefore, this study proposes a novel multi-factor intelligent recognition-based ensemble forecasting system for interval-valued carbon price forecasts. In this system, factors from multiple perspectives are considered to analyze interval-valued carbon price fluctuations. To select the optimal set of influencing factors, a multi-factor intelligent recognition subsystem combining a time-series causal analysis method with multi-objective feature selection algorithms was developed. This subsystem simultaneously considers the intrinsic correlations among factors and the predictive performance to thereby ensure the accuracy of feature selection while reducing redundancy. Additionally, an ensemble forecasting subsystem integrating multiple machine learning models was constructed to exploit the merits of each model and realize more accurate results than can be achieved by any individual model. Empirical research demonstrated that this forecasting system could accurately identify powerful influencing factors, outperform other feature selection strategies, and achieve interval-valued mean absolute percentage errors of 1.5883 % and 1.5113 %, respectively. Therefore, this system is an effective tool for predicting carbon prices.