Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression
journal contributionposted on 17.06.2019, 10:09 by Bangzhu Zhu, Dong Han, Ping Wang, Zhanchi Wu, Tao Zhang, Yi-Ming Wei
Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon price forecasting. Firstly, each carbon price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original carbon price. Finally, using four different-matured carbon futures prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances.
National Natural Science Foundation of China (NSFC) (71473180, 71201010, 71303174, 71303076, and 71673083), National Philosophy and Social Science Foundation of China (14AZD068, 15ZDA054, 16ZZD049), Guangdong Young Zhujiang Scholar (Yue Jiaoshi 95), Natural Science Foundation for Distinguished Young Talents of Guangdong (2014A030306031), Soft Science Foundation of Guangdong (2014A070703062), Social Science Foundation of Guangdong (GD14XYJ21), Distinguished Young Teachers of Guangdong (145), High-level Personnel Project of Guangdong (246), Guangdong Key Base of Humanities and Social Science—Enterprise Development Research Institute, Institute of Resource, Environment and Sustainable Development Research, and Guangzhou key Base of Humanities and Social Science—Centre for Low Carbon Economic Research.
- Loughborough University London