Forecasting Crude Oil Prices : A Deep Learning based Model

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

10 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)300-307
Journal / PublicationProcedia Computer Science
Volume122
Early online date12 Dec 2017
Publication statusPublished - 2017

Conference

TitleThe Fifth International Conference on Information Technology and Quantitative Management (ITQM 2017)
Locationhttp://itqm-meeting.org/2017/
PlaceIndia
CityNew Delhi
Period8 - 10 December 2017

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Abstract

With the popularity of the deep learning model in the engineering fields, it has attracted significant research interests in the economic and finance fields. In this paper, we use the deep learning model to capture the unknown complex nonlinear characteristics of the crude oil price movement. We further propose a new hybrid crude oil price forecasting model based on the deep learning model. Using the proposed model, major crude oil price movement is analyzed and modeled. The performance of the proposed model is evaluated using the price data in the WTI crude oil markets. The empirical results show that the proposed model achieves the improved forecasting accuracy.

Research Area(s)

  • ARMA model, Crude oil price forecasting, Deep Learning model, Random Walk model

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