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

Bangzhu Zhu, Dong Han, Ping Wang, Zhanchi Wu, Tao Zhang, Yi-Ming Wei

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    87 Citations (Scopus)
    543 Downloads (Pure)

    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.
    Original languageEnglish
    Pages (from-to)521-530
    Number of pages10
    JournalApplied Energy
    Volume191
    Early online date9 Feb 2017
    DOIs
    Publication statusPublished - 1 Apr 2017

    Keywords

    • Carbon price forecasting
    • Empirical mode decomposition
    • Least squares support vector regression
    • Particle Swarm Optimization (PSO)

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