A decomposition-ensemble-integration framework for carbon price forecasting

Accurate carbon price forecasting is crucial for effective carbon trading policies that mitigate climate change. The volatility and uncertainty of carbon prices pose significant challenges. Traditional frameworks that use a single model for all intrinsic mode functions (IMFs) fail to leverage the diverse strengths of different models. In this paper, we propose DecEnsInt, a novel framework that utilizes an ensemble of models tailored to distinct frequency domains. By using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose historical prices into IMFs and optimizing ensemble parameters through Sequential Least Squares Programming (SLSQP) for each frequency domain, DecEnsInt enhances forecasting performance. Extensive experiments on four Emissions Trading System (ETS) datasets, evaluated using RMSE, MAPE, MAE and R², demonstrate DecEnsInt's superiority, achieving relative improvements in key metrics by 10.58% to 17.72% over runner-up ensemble methods. DecEnsInt also achieves better robustness and stability across different forecasting steps, proving its effectiveness in handling the complexities of carbon price forecasting.