A multi-scale method for forecasting oil price with multi-factor search engine data

doi:10.1016/j.apenergy.2019.114033

CSpace

	A multi-scale method for forecasting oil price with multi-factor search engine data
	Tang, Ling 1; Zhang, Chengyuan 1; Li, Ling 2; Wang, Shouyang 3
	2020
发表期刊	APPLIED ENERGY
ISSN	0306-2619
卷号	257 页码:12
摘要	With the boom in big data, a promising idea for using search engine data has emerged and improved international oil price prediction, a hot topic in the fields of energy system modelling and analysis. Since different search engine data drive the oil price in different ways at different timescales, a multi-scale forecasting methodology is proposed that carefully explores the multi-scale relationship between the oil price and multi-factor search engine data. In the proposed methodology, three major steps are involved: (1) a multi-factor data process, to collect informative search engine data, reduce dimensionality, and test the predictive power via statistical analyses; (2) multi-scale analysis, to extract matched common modes at similar timescales from the oil price and multi-factor search engine data via multivariate empirical mode decomposition; (3) oil price prediction, including individual prediction at each timescale and ensemble prediction across timescales via a typical forecasting technique. With the Brent oil price as a sample, the empirical results show that the novel methodology significantly outperforms its original form (without multi-factor search engine data and multi-scale analysis), semi-improved versions (with either multi-factor search engine data or multi-scale analysis), and similar counterparts (with other multi-scale analysis), in both the level and directional predictions.
关键词	Big data Search engine data Google trends Multivariate empirical mode decomposition Oil price forecasting
DOI	10.1016/j.apenergy.2019.114033
收录类别	SCI
语种	英语
资助项目	National Science Fund for Outstanding Young Scholars[71622011] ; National Science Fund for Outstanding Young Scholars[71971007] ; National Natural Science Foundation of China[71433001] ; National Program for Support of Top Notch Young Professionals
WOS研究方向	Energy & Fuels ; Engineering
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS记录号	WOS:000506574700005
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/50601
专题	中国科学院数学与系统科学研究院
通讯作者	Tang, Ling
作者单位	1.Beihang Univ, Sch Econ & Management, 37 Xueyuan Rd, Beijing 100191, Peoples R China 2.Capital Univ Econ & Business, Int Sch Econ & Management, Beijing 100070, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Tang, Ling,Zhang, Chengyuan,Li, Ling,et al. A multi-scale method for forecasting oil price with multi-factor search engine data[J]. APPLIED ENERGY,2020,257:12.
APA	Tang, Ling,Zhang, Chengyuan,Li, Ling,&Wang, Shouyang.(2020).A multi-scale method for forecasting oil price with multi-factor search engine data.APPLIED ENERGY,257,12.
MLA	Tang, Ling,et al."A multi-scale method for forecasting oil price with multi-factor search engine data".APPLIED ENERGY 257(2020):12.