Oil price prediction using a supervised neural network
by Adnan Khashman; Claudia-Georgeta Carstea
International Journal of Oil, Gas and Coal Technology (IJOGCT), Vol. 20, No. 3, 2019

Abstract: Many attempts have been made at designing efficient prediction systems for oil price due to the impact of oil on the world's economy. In this paper, we present an efficient oil price prediction system based on using a supervised neural network. The neural model receives at its input novel key economic and seasonal indicators, which we extract from the West Texas Intermediate (WTI) dataset of crude oil prices over 24 years. The model predicts at its output weekly oil prices within five dollars/barrel accuracy. The experimental results reveal that the obtained correct prediction rate of 88% is higher than rates reported in other related works, thus indicating that neural networks can be effectively used for predicting oil prices. [Received: August 3, 2016; Accepted: February 17, 2017]

Online publication date: Mon, 25-Mar-2019

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Oil, Gas and Coal Technology (IJOGCT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com