Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression
By Bangzhu Zhu, Dong Han, Ping Wang, Zhanchi Wu, Zhang Tao, Yi-Ming Wei. 2017
276 citations recorded by the source corpus.
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.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/c7d2eb417039b5a5e63f0f4c21f0ba4177f263c7
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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Lynn H. Kaack
- Yoshua Bengio
- Natasha Jaques
- Konrad Paul Kording
- Tegan Maharaj
- Felix Creutzig
- Demis Hassabis
- Yi Yang
In Electrical and Electronic Engineering
- Demis Hassabis
- Alexandra Sasha Luccioni
- Lynn H. Kaack
- Yoshua Bengio
- Natasha Jaques
- Konrad Paul Kording
- Felix Creutzig
- Aimee van Wynsberghe
In Management Science and Operations Research
- Jennifer Tour Chayes
- Yoshua Bengio
- Andrew Slavin Ross
- Alexandra Sasha Luccioni
- Natasha Jaques
- Tegan Maharaj
- Felix Creutzig
- Demis Hassabis
Citations in the corpus
- Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors (2019, unknown): “[21] established an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon prices and showed more robust performance.”
- Tackling Climate Change with Machine Learning (2019, methodology): “[771] Bangzhu Zhu, Dong Han, Ping Wang, Zhanchi Wu, Tao Zhang, and Yi-Ming Wei.”