Work

SW-HyDEC: A structure-aware hybrid decomposition and ensemble learning framework for carbon price forecasting

Year: 2026

Type: article

Abstract: Accurate carbon price forecasting is essential for market efficiency, effective climate policy design, and strategic decision-making by firms managing carbon assets. Carbon prices reflect a dynamic interplay of emission quotas, economic conditions, and policy expectations, directly influencing emission reduction incentives and investment in green technologies. However, existing models often treat data input variables independently and struggle with the non-stationary, multi-scale nature of carbon price signals. This paper proposes SW-HyDEC, a structure-aware hybrid framework that integrates Correlation-based Feature Selection (CFS), Recursive Feature Elimination (RFE), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and a Hybrid-Kernel Extreme Learning Machine (Hybrid-KELM). To enhance the training convergence and robustness, model parameters are optimized using a Sobol-sequence enhanced Whale Optimization Algorithm (Sobol-WOA). Experimental results on real-world carbon market datasets demonstrate that SW-HyDEC significantly outperforms mainstream statistical, machine learning, and deep learning baselines in terms of accuracy, generalization, and stability.

Source: Neural Computing and Applications

DOI: 10.1007/s00521-026-11982-8

Authors: Qingwen Zeng, Zhaoge Bi, Lining Chen, Huaming Chen

Institution: The University of Sydney

Language: English

Topic: Stock Market Forecasting Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Climate action, Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,290