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作者:

Li, Hongtao (Li, Hongtao.) | Jin, Feng (Jin, Feng.) | Sun, Shaolong (Sun, Shaolong.) | Li, Yongwu (Li, Yongwu.)

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摘要:

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.

关键词:

Sample entropy calculation Carbon price forecasting Secondary decomposition ensemble approach Improved optimization algorithm

作者机构:

  • [ 1 ] [Li, Hongtao]Lanzhou Jiaotong Univ, Sch Traff & Transportat, Lanzhou 730070, Peoples R China
  • [ 2 ] [Jin, Feng]Lanzhou Jiaotong Univ, Sch Traff & Transportat, Lanzhou 730070, Peoples R China
  • [ 3 ] [Sun, Shaolong]Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China
  • [ 4 ] [Li, Yongwu]Beijing Univ Technol, Res Base Beijing Modern Mfg Dev, Coll Econ & Management, Beijing 100124, Peoples R China

通讯作者信息:

  • [Sun, Shaolong]Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China

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来源 :

KNOWLEDGE-BASED SYSTEMS

ISSN: 0950-7051

年份: 2021

卷: 214

8 . 8 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:87

JCR分区:1

被引次数:

WoS核心集被引频次: 98

SCOPUS被引频次: 102

ESI高被引论文在榜: 0 展开所有

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