Interval decomposition ensemble approach for crude oil price forecasting

doi:10.1016/j.eneco.2018.10.015

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	Interval decomposition ensemble approach for crude oil price forecasting
	Sun, Shaolong 1,2,3; Sun, Yuying1,4 ; Wang, Shouyang1,2,4 ; Wei, Yunjie1,4
刊名	ENERGY ECONOMICS
	2018-10-01
卷号	76 页码:274-287
关键词	Bivariate empirical mode decomposition Crude oil price forecasting Interval-valued time series Interval Holt's method Interval neural networks
ISSN号	0140-9883
DOI	10.1016/j.eneco.2018.10.015
英文摘要	Crude 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.
资助项目	National Natural Science Foundation of China[71801213] ; National Natural Science Foundation of China[71771208] ; National Natural Science Foundation of China[71642006]
WOS研究方向	Business & Economics
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000453498400018
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/31915]
专题	系统科学研究所
通讯作者	Wei, Yunjie
作者单位	1.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
推荐引用方式 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.