Ensemble forecasting system based on decomposition-selection-optimization for point and interval carbon price prediction

Abstract

In this study, a novel ensemble forecasting system based on data decomposition, feature selection, optimal sub-model determination and the improved multi-objective optimization is designed to conduct both point and interval carbon price predictions. To testify the performance of the developed system, three daily carbon price series are collected for experiments and verification. The results indicated that the ensemble system has achieved the optimal mean absolute percent errors of 0.9267, 1.6433 and 0.9303% for Sites 1–3 during point forecasting. In addition, the prediction interval coverage probabilities of three sites are 0.9800, 0.9600 and 0.9600 under the 95% confidence level for interval forecasting, which demonstrated that the developed system can provide scientific and sufficient reference for carbon trading markets.