A hybrid framework for carbon trading price forecasting: The role of multiple influence factor

Abstract

Current carbon trading price forecasting research ignores the significance of multiple influence factors, feature selection, preprocessing of the carbon price and its exogenous variables, multi-objective intelligence optimization and kernel-based models in terms of improving prediction validity, which may lead to an undesirable forecasting performance. As a result, a novel hybrid forecasting framework that considers multiple influence factors is developed for carbon price forecasting, which enjoys the merits of some new algorithms and successfully overcomes the challenges of multiple influence factor-based carbon price forecasting. Specifically, both the original carbon price and its exogenous variables benefit from advanced data preprocessing technology. Moreover, feature selection is proposed to determine the optimal feature for modeling the carbon price. Meanwhile, to overcome the limitations of the extreme learning machine and traditional artificial neural network models and obtain desirable, accurate and stable forecasts, the optimal kernel-based extreme learning machine model with good generalizability and stability is developed, grounded in a newly proposed optimizer named the multi-objective chaotic sine cosine algorithm. Experiments, analyses and discussion of the results prove that the developed framework outperforms all compared models which can be as an effective forecasting tool for forecasting and management of the carbon trading market.