Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework

doi:10.1016/j.resourpol.2022.102737

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	Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework
	Guo, Jingjun1 ; Zhao, Zhengling1 ; Sun, Jingyun1 ; Sun, Shaolong 2
	2022-08-01
Source Publication	Resources Policy
Volume	77
Abstract	Crude oil is an important global commodity, and its price fluctuation affects the political and economic security of a country. Therefore, it is necessary to conduct crude oil price forecasting. Based on the forecasting research of multi-source information and decomposition-ensemble, we combine the two into a model and propose a multi-perspective crude oil price forecasting model under a new decomposition-ensemble framework. Specifically, the crude oil price series is decomposed and reconstructed into several modes through variational mode decomposition (VMD) and fuzzy entropy (FE). Further, we screen the effective predictors from structured and unstructured multi-source data using the Granger causality test, and select the optimal input features through random forest - recursive feature elimination (RF-RFE). Finally, each reconstruction mode is individually forecasted on the basis of the selected different input features and the forecasting values obtained are combined and integrated; the final result is obtained from the integrating prediction results through the error evaluation criterion. The West Texas Intermediate (WTI) daily spot price is adopted to validate the performance of our proposed model. The empirical results show that compared with the benchmark models, the proposed model can significantly improve forecasting accuracy. © 2022 Elsevier Ltd
Keyword	Decision trees Forecasting Crude oil price forecasting Crude oil spot price forecasting Internet concern Long short term memory network Macroeconomic variables Memory network Mode decomposition Price forecasting Spot price Variational mode decomposition
DOI	10.1016/j.resourpol.2022.102737
Indexed By	EI
ISSN	0301-4207
Language	英语
Publisher	Elsevier Ltd
EI Accession Number	20221812056626
EI Keywords	Crude oil
EI Classification Number	512.1 Petroleum Deposits ; 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory ; 961 Systems Science
Original Document Type	Journal article (JA)
Document Type	期刊论文
Identifier	http://ir.lzufe.edu.cn/handle/39EH0E1M/32757
Collection	统计学院
Affiliation	1.School of Statistics, Lanzhou University of Finance and Economics, Lanzhou; 730020, China; 2.School of Management, Xi'an Jiaotong University, Xi'an; 710049, China
First Author Affilication	School of Statistics
Recommended Citation GB/T 7714	Guo, Jingjun,Zhao, Zhengling,Sun, Jingyun,et al. Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework[J]. Resources Policy,2022,77.
APA	Guo, Jingjun,Zhao, Zhengling,Sun, Jingyun,&Sun, Shaolong.(2022).Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework.Resources Policy,77.
MLA	Guo, Jingjun,et al."Multi-perspective crude oil price forecasting with a new decomposition-ensemble framework".Resources Policy 77(2022).

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