A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting
Sun, Jingyun1,2; Zhao, Panpan1; Sun, Shaolong3
2022-08-01
Source PublicationResources Policy
Volume77
AbstractThis 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
KeywordCosts Crude oil Entropy Financial markets Forecasting Knowledge acquisition Machine learning Signal processing 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
DOI10.1016/j.resourpol.2022.102762
Indexed ByEI
ISSN0301-4207
Language英语
PublisherElsevier Ltd
EI Accession Number20222112136689
EI KeywordsLearning algorithms
EI Classification Number512.1 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
Original Document TypeJournal article (JA)
Document Type期刊论文
Identifierhttp://ir.lzufe.edu.cn/handle/39EH0E1M/32760
Collection统计学院
Affiliation1.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
First Author AffilicationSchool of Statistics
Recommended Citation
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).
Files in This Item:
There are no files associated with this item.
Related Services
Usage statistics
Google Scholar
Similar articles in Google Scholar
[Sun, Jingyun]'s Articles
[Zhao, Panpan]'s Articles
[Sun, Shaolong]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Jingyun]'s Articles
[Zhao, Panpan]'s Articles
[Sun, Shaolong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Jingyun]'s Articles
[Zhao, Panpan]'s Articles
[Sun, Shaolong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.