Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression

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Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression. / Zhu, Bangzhu; Han, Dong; Wang, Ping; Wu, Zhanchi; Zhang, Tao; Wei, Yi-Ming.

In: Applied Energy, Vol. 191, 01.04.2017, p. 521-530.

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Zhu, Bangzhu ; Han, Dong ; Wang, Ping ; Wu, Zhanchi ; Zhang, Tao ; Wei, Yi-Ming. / Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression. In: Applied Energy. 2017 ; Vol. 191. pp. 521-530.

Bibtex

@article{fda4d484970541df97c7b5196ed648c8,
title = "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression",
abstract = "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.",
keywords = "Carbon price forecasting, Empirical mode decomposition, Least squares support vector regression, Particle Swarm Optimization (PSO)",
author = "Bangzhu Zhu and Dong Han and Ping Wang and Zhanchi Wu and Tao Zhang and Yi-Ming Wei",
year = "2017",
month = apr
day = "1",
doi = "10.1016/j.apenergy.2017.01.076",
language = "English",
volume = "191",
pages = "521--530",
journal = "Applied Energy",
issn = "0306-2619",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression

AU - Zhu, Bangzhu

AU - Han, Dong

AU - Wang, Ping

AU - Wu, Zhanchi

AU - Zhang, Tao

AU - Wei, Yi-Ming

PY - 2017/4/1

Y1 - 2017/4/1

N2 - 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.

AB - 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.

KW - Carbon price forecasting

KW - Empirical mode decomposition

KW - Least squares support vector regression

KW - Particle Swarm Optimization (PSO)

U2 - 10.1016/j.apenergy.2017.01.076

DO - 10.1016/j.apenergy.2017.01.076

M3 - Article

VL - 191

SP - 521

EP - 530

JO - Applied Energy

JF - Applied Energy

SN - 0306-2619

ER -