Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
- Zhu, Bangzhu & Wei, Yiming, 2013. "Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology," Omega, Elsevier, vol. 41(3), pages 517-524.
- repec:dau:papers:123456789/6515 is not listed on IDEAS
- Benz, Eva & Trück, Stefan, 2009. "Modeling the price dynamics of CO2 emission allowances," Energy Economics, Elsevier, vol. 31(1), pages 4-15, January.
- Qi, Shaozhou & Wang, Banban & Zhang, Jihong, 2014. "Policy design of the Hubei ETS pilot in China," Energy Policy, Elsevier, vol. 75(C), pages 31-38.
- Han, Meng & Ding, Lili & Zhao, Xin & Kang, Wanglin, 2019. "Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors," Energy, Elsevier, vol. 171(C), pages 69-76.
- Guoqiang Sun & Tong Chen & Zhinong Wei & Yonghui Sun & Haixiang Zang & Sheng Chen, 2016. "A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks," Energies, MDPI, vol. 9(1), pages 1-16, January.
- Jianguo Zhou & Xuejing Huo & Xiaolei Xu & Yushuo Li, 2019. "Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm," Energies, MDPI, vol. 12(5), pages 1-22, March.
- Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
- Song, Yazhi & Liu, Tiansen & Liang, Dapeng & Li, Yin & Song, Xiaoqiu, 2019. "A Fuzzy Stochastic Model for Carbon Price Prediction Under the Effect of Demand-related Policy in China's Carbon Market," Ecological Economics, Elsevier, vol. 157(C), pages 253-265.
- Dégerine, Serge & Lambert-Lacroix, Sophie, 2003. "Characterization of the partial autocorrelation function of nonstationary time series," Journal of Multivariate Analysis, Elsevier, vol. 87(1), pages 46-59, October.
- Gary Koop & Lise Tole, 2013.
"Forecasting the European carbon market,"
Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 723-741, June.
- Koop, Gary & Tole, Lise, 2011. "Forecasting the European Carbon Market," SIRE Discussion Papers 2011-20, Scottish Institute for Research in Economics (SIRE).
- Gary Koop & Lise Tole, 2011. "Forecasting the European Carbon Market," Working Papers 1110, University of Strathclyde Business School, Department of Economics.
- Julien Chevallier, 2011. "Wavelet packet transforms analysis applied to carbon prices," Economics Bulletin, AccessEcon, vol. 31(2), pages 1731-1747.
- Jianguo Zhou & Xuechao Yu & Xiaolei Yuan, 2018. "Predicting the Carbon Price Sequence in the Shenzhen Emissions Exchange Using a Multiscale Ensemble Forecasting Model Based on Ensemble Empirical Mode Decomposition," Energies, MDPI, vol. 11(7), pages 1-17, July.
- Sizhou Sun & Lisheng Wei & Jie Xu & Zhenni Jin, 2019. "A New Wind Speed Forecasting Modeling Strategy Using Two-Stage Decomposition, Feature Selection and DAWNN," Energies, MDPI, vol. 12(3), pages 1-24, January.
- Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
- Bangzhu Zhu, 2012. "A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network," Energies, MDPI, vol. 5(2), pages 1-16, February.
- Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yunhe Cheng & Beibei Hu, 2022. "Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine," Energies, MDPI, vol. 15(10), pages 1-18, May.
- Xiaohua Song & Wen Zhang & Zeqi Ge & Siqi Huang & Yamin Huang & Sijia Xiong, 2022. "A Study of the Influencing Factors on the Carbon Emission Trading Price in China Based on the Improved Gray Relational Analysis Model," Sustainability, MDPI, vol. 14(13), pages 1-27, June.
- Jinhan Yu & Licheng Sun, 2022. "Supply Chain Emission Reduction Decisions, Considering Overconfidence under Conditions of Carbon Trading Price Volatility," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
- Lei Su & Wenjiao Yu & Zhongxuan Zhou, 2023. "Global Trends of Carbon Finance: A Bibliometric Analysis," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Jianguo Zhou & Dongfeng Chen, 2021. "Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
- Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
- Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
- Jianguo Zhou & Shiguo Wang, 2021. "A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors," Energies, MDPI, vol. 14(5), pages 1-20, March.
- Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
- Jianguo Zhou & Xuejing Huo & Xiaolei Xu & Yushuo Li, 2019. "Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm," Energies, MDPI, vol. 12(5), pages 1-22, March.
- Wang, Piao & Tao, Zhifu & Liu, Jinpei & Chen, Huayou, 2023. "Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode," Energy Economics, Elsevier, vol. 118(C).
- Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
- Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
- Peng Chen & Andrew Vivian & Cheng Ye, 2022. "Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine," Annals of Operations Research, Springer, vol. 313(1), pages 559-601, June.
- Ding, Lili & Zhang, Rui & Zhao, Xin, 2024. "Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks," Energy, Elsevier, vol. 288(C).
- Niu, Xinsong & Wang, Jiyang & Wei, Danxiang & Zhang, Lifang, 2022. "A combined forecasting framework including point prediction and interval prediction for carbon emission trading prices," Renewable Energy, Elsevier, vol. 201(P1), pages 46-59.
- Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
- Yumin Li & Ruiqi Yang & Xiaoman Wang & Jiaming Zhu & Nan Song, 2023. "Carbon Price Combination Forecasting Model Based on Lasso Regression and Optimal Integration," Sustainability, MDPI, vol. 15(12), pages 1-26, June.
- Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
- Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018. "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, vol. 70(C), pages 143-157.
- Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
- Wei Sun & Junjian Zhang, 2020. "Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors," Energies, MDPI, vol. 13(13), pages 1-22, July.
- Xu, Hua & Wang, Minggang & Jiang, Shumin & Yang, Weiguo, 2020. "Carbon price forecasting with complex network and extreme learning machine," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
- Quande Qin & Huangda He & Li Li & Ling-Yun He, 2020. "A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1249-1273, April.
More about this item
Keywords
carbon price forecasting; secondary decomposition; complementary ensemble empirical mode decomposition with adaptive noise; bald eagle search algorithm; extreme learning machine;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:15:p:8413-:d:603128. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.