Work

A Multi-granularity Heterogeneous Ensemble Model for Point and Interval Forecasting of Carbon Prices

Year: 2025

Type: article

Abstract: The inherent complexities of carbon price fluctuations and the variability of influencing factors make the accurate prediction of carbon emission prices a significant challenge. This study introduces a novel multi-granularity heterogeneous ensemble model for point and interval forecasting. First, three feature selection methods are used to identify key factors affecting carbon price and to construct multiple granular spaces. Second, the distinct features identified through feature selection methods are input into six point forecasting models. Third, the Grey Wolf Optimization (GWO) algorithm is applied to calculate the optimal weights for each individual model. Finally, the interval predictions of carbon prices are obtained by integrating the point predictions with the kernel density estimation (KDE) model. The research results indicate that the proposed model outperforms comparative models in both predictive accuracy and statistical validation, showcasing outstanding predictive performance.

Source: International Journal of Computational Intelligence Systems

DOI: 10.1007/s44196-025-00881-w

Authors: Di Sha, Xianyi Zeng, Kim-Phuc Tran, Lin Xia, Ruolin Wang

Institutions: Jiangnan University, Université de Lille, GEnie des Matériaux TEXtiles, École Nationale Supérieure des Arts et Industries Textiles, Dong A University

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,490