A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting

doi:10.1016/j.resourpol.2022.102762

	A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting
	Sun, Jingyun1,2 ; Zhao, Panpan1 ; Sun, Shaolong 3
	2022-08
发表期刊	Resources Policy
卷号	77
摘要	This study proposes a new method for crude oil future price forecasting. The original crude oil futures price series is decomposed into a series of sub-sequences using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, and the permutation entropy (PE) method is employed to reconstruct these sub-sequences into high-frequency, low-frequency, and trend components. Using the kernel extreme learning machine (KELM) optimised by the chaotic sparrow search algorithm (CSSA), the low-frequency component and trend component are predicted. However, the high-frequency component is decomposed secondary to the empirical mode decomposition (EMD) method, and the PE and CSSA-KELM models are employed again to obtain the linear integrating prediction result for the high-frequency component. Finally, the forecasting results of the high-frequency, low-frequency, and trend components are nonlinearly integrated with the CSSA-KELM model, and the final forecasting value for crude oil futures prices is obtained. To verify the effectiveness of the proposed model, we empirically forecast the Brent and WTI crude oil futures prices. The empirical results show that the approach proposed in this study improves forecasting accuracy compared to other benchmark models and has good robustness. © 2022 Elsevier Ltd
关键词	Knowledge acquisition Costs Machine learning Crude oil Empirical mode decomposition Learning algorithms Entropy Chaotic sparrow search algorithm Chaotics Crude oil forecasting Crude oil futures Kernel extreme learning machine Oil forecasting Oil futures price Permutation entropy Search Algorithms Secondary decomposition
DOI	10.1016/j.resourpol.2022.102762
收录类别	EI ; SSCI
ISSN	0301-4207
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Studies
WOS记录号	WOS:000830106600003
出版者	Elsevier Ltd
EI入藏号	20222112136689
EI主题词	Forecasting
EI分类号	512.1 Petroleum Deposits ; 641.1 Thermodynamics ; 716.1 Information Theory and Signal Processing ; 723.4 Artificial Intelligence ; 723.4.2 Machine Learning ; 911 Cost and Value Engineering ; Industrial Economics
原始文献类型	Journal article (JA)
EISSN	1873-7641
引用统计	被引频次：33[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.lzufe.edu.cn/handle/39EH0E1M/33208
专题	统计与数据科学学院
作者单位	1.School of Statistics, Lanzhou University of Finance and Economics, Lanzhou; 730020, China; 2.Center for Quantitative Analysis of Gansu Economic Development, Lanzhou; 730020, China; 3.School of Management, Xi'an Jiaotong University, Xi'an; 710049, China
第一作者单位	统计与数据科学学院
推荐引用方式 GB/T 7714	Sun, Jingyun,Zhao, Panpan,Sun, Shaolong. A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting[J]. Resources Policy,2022,77.
APA	Sun, Jingyun,Zhao, Panpan,&Sun, Shaolong.(2022).A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting.Resources Policy,77.
MLA	Sun, Jingyun,et al."A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting".Resources Policy 77(2022).