期刊
KNOWLEDGE-BASED SYSTEMS
卷 214, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.knosys.2020.106686
关键词
Carbon price forecasting; Secondary decomposition ensemble approach; Sample entropy calculation; Improved optimization algorithm
资金
- National Natural Science Foundation of China [11361031]
- Science and Technology of Gansu Province Fund Project, China [20JR5RA394]
- Fundamental Research Funds for the Central Universities, China [xpt012020022]
- Beijing Natural Science Foundation, China [9192001]
- General Projects of Social Science Program of Beijing Municipal Commission of Education, China [SM202010005005]
- LanZhouJiaoTong University -TianJin University Innovation Fund Project, China [2018064]
The paper presents a new secondary decomposition strategy for forecasting carbon prices, showing superior performance in multistep forecasting compared to benchmark models.
The forecasting of carbon price plays a significant role in gaining insight into the dynamics of carbon market around the world and assigning quota about carbon emissions. Many studies have shown that decomposing the original data into several components with similar attributes is a widely accepted method addressing highly complex data. The resulting issue is that the high complexity of some components obtained is still tricky. This paper develops a new secondary decomposition strategy, which employs the complementary ensemble empirical mode decomposition (CEEMD) and the variational mode decomposition (VMD) to decompose the original series and the acquired intrinsic mode functions (IMFs) with maximum sample entropy value, respectively. All components are forecasted, including these generated by the first and secondary decomposition. The final results are obtained by synthesizing the predictions of all components. The experimental study states clearly that the established approach is superior to all benchmark models in terms of multistep horizons forecasting, and can provide the reliable and convincing results. (C) 2020 Elsevier B.V. All rights reserved.
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