CSpace  > 系统科学研究所
Interval decomposition ensemble approach for crude oil price forecasting
Sun, Shaolong1,2,3; Sun, Yuying1,4; Wang, Shouyang1,2,4; Wei, Yunjie1,4
2018-10-01
Source PublicationENERGY ECONOMICS
ISSN0140-9883
Volume76Pages:274-287
AbstractCrude oil is one of the most important energy sources in the world, and it is very important for policymakers, enterprises and investors to forecast the price of crude oil accurately. This paper proposes an interval decomposition ensemble (IDE) learning approach to forecast interval-valued crude oil price by integrating bivariate empirical mode decomposition (BEMD), interval MLP (MLPI) and interval exponential smoothing method (Holt(I)). Firstly, the original interval-valued crude oil price is transformed into a complex-valued signal. Secondly, BEMD is used to decompose the constructed complex-valued signal into a finite number of complex-valued intrinsic mode functions (IMFs) components and one complex-valued residual component. Thirdly, MLPI is used to simultaneously forecast the lower and the upper bounds of each IMF (non-linear patterns), and Holt(I) is used for modeling the residual component (linear pattern). Finally, the forecasting results of the lower and upper bounds of all the components are combined to generate the aggregated interval-valued output by employing another MLPI as the ensemble tool. The empirical results show that our proposed IDE learning approach with different forecasting horizons and different data frequencies significantly outperforms some other benchmark models by means of forecasting accuracy and hypothesis tests. (C) 2018 Elsevier B.V. All rights reserved.
KeywordBivariate empirical mode decomposition Crude oil price forecasting Interval-valued time series Interval Holt's method Interval neural networks
DOI10.1016/j.eneco.2018.10.015
Language英语
Funding ProjectNational Natural Science Foundation of China[71801213] ; National Natural Science Foundation of China[71771208] ; National Natural Science Foundation of China[71642006]
WOS Research AreaBusiness & Economics
WOS SubjectEconomics
WOS IDWOS:000453498400018
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31916
Collection系统科学研究所
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
3.City Univ Hong Kong, Sch Data Sci, Kowloon, Tat Chee Ave, Hong Kong, Peoples R China
4.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Sun, Shaolong,Sun, Yuying,Wang, Shouyang,et al. Interval decomposition ensemble approach for crude oil price forecasting[J]. ENERGY ECONOMICS,2018,76:274-287.
APA Sun, Shaolong,Sun, Yuying,Wang, Shouyang,&Wei, Yunjie.(2018).Interval decomposition ensemble approach for crude oil price forecasting.ENERGY ECONOMICS,76,274-287.
MLA Sun, Shaolong,et al."Interval decomposition ensemble approach for crude oil price forecasting".ENERGY ECONOMICS 76(2018):274-287.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Sun, Shaolong]'s Articles
[Sun, Yuying]'s Articles
[Wang, Shouyang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Sun, Shaolong]'s Articles
[Sun, Yuying]'s Articles
[Wang, Shouyang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Sun, Shaolong]'s Articles
[Sun, Yuying]'s Articles
[Wang, Shouyang]'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.