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Crude oil price forecasting based on internet concern using an extreme learning machine
Wang, Jue1,2; Athanasopoulos, George3; Hyndman, Rob J.3; Wang, Shouyang1,2
2018-10-01
Source PublicationINTERNATIONAL JOURNAL OF FORECASTING
ISSN0169-2070
Volume34Issue:4Pages:665-677
AbstractThe growing internet concern (IC) over the crude oil market and related events influences market trading, thus creating further instability within the oil market itself. We propose a modeling framework for analyzing the effects of IC on the oil market and for predicting the price volatility of crude oil's futures market. This novel approach decomposes the original time series into intrinsic modes at different time scales using bivariate empirical mode decomposition (BEMD). The relationship between the oil price volatility and IC at an individual frequency is investigated. By utilizing decomposed intrinsic modes as specified characteristics, we also construct extreme learning machine (ELM) models with variant forecasting schemes. The experimental results illustrate that ELM models that incorporate intrinsic modes and IC outperform the baseline ELM and other benchmarks at distinct horizons. Having the power to improve the accuracy of baseline models, internet searching is a practical way of quantifying investor attention, which can help to predict short-run price fluctuations in the oil market. (C) 2018 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
KeywordCrude oil futures price Internet concern BEMD ELM
DOI10.1016/j.ijforecast.2018.03.009
Language英语
Funding ProjectYouth Innovation Promotion Association, CAS[2014004] ; National Center for Mathematics and Interdisciplinary Sciences (NCMIS)[629092ZZ1] ; CAS ; National Natural Science Foundation of China (NSFC)[71771208] ; National Natural Science Foundation of China (NSFC)[71271202]
WOS Research AreaBusiness & Economics
WOS SubjectEconomics ; Management
WOS IDWOS:000447104600008
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31394
Collection系统科学研究所
Affiliation1.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
2.Chinese Acad Sci, CEFS, MADIS, Acad Math & Syst Sci, Beijing 100190, Peoples R China
3.Monash Univ, Dept Econometr & Business Stat, Clayton, Vic, Australia
Recommended Citation
GB/T 7714
Wang, Jue,Athanasopoulos, George,Hyndman, Rob J.,et al. Crude oil price forecasting based on internet concern using an extreme learning machine[J]. INTERNATIONAL JOURNAL OF FORECASTING,2018,34(4):665-677.
APA Wang, Jue,Athanasopoulos, George,Hyndman, Rob J.,&Wang, Shouyang.(2018).Crude oil price forecasting based on internet concern using an extreme learning machine.INTERNATIONAL JOURNAL OF FORECASTING,34(4),665-677.
MLA Wang, Jue,et al."Crude oil price forecasting based on internet concern using an extreme learning machine".INTERNATIONAL JOURNAL OF FORECASTING 34.4(2018):665-677.
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