Forecasting Crude Oil Prices : A Deep Learning based Model
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 300-307 |
Journal / Publication | Procedia Computer Science |
Volume | 122 |
Online published | 12 Dec 2017 |
Publication status | Published - 2017 |
Conference
Title | The Fifth International Conference on Information Technology and Quantitative Management (ITQM 2017) |
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Location | http://itqm-meeting.org/2017/ |
Place | India |
City | New Delhi |
Period | 8 - 10 December 2017 |
Link(s)
DOI | DOI |
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Attachment(s) | Documents
Publisher's Copyright Statement
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85040312106&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(43f28f50-6c13-4ff5-a02e-bb87b4c67a24).html |
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
Citation Format(s)
Forecasting Crude Oil Prices: A Deep Learning based Model. / Chen, Yanhui; He, Kaijian; Tso, Geoffrey K.F.
In: Procedia Computer Science, Vol. 122, 2017, p. 300-307.
In: Procedia Computer Science, Vol. 122, 2017, p. 300-307.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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