Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction

•We propose approach for the prediction of the WTI crude oil price.•The values predicted by the proposed method and actual once are statistically equal.•The proposed method indicated performance improvement over existing results. This paper proposes an alternative approach based on a genetic algorit...

Full description

Saved in:
Bibliographic Details
Published in:Applied energy Vol. 142; pp. 266 - 273
Main Authors: Chiroma, Haruna, Abdulkareem, Sameem, Herawan, Tutut
Format: Journal Article
Language:English
Published: Elsevier Ltd 15.03.2015
Subjects:
algorithms
Backpropagation algorithms
energy costs
Genetic Algorithm
issues and policy
Neural Network
neural networks
prediction
prices
West Texas Intermediate crude oil price
Neural Network
Genetic Algorithm
Backpropagation algorithms
West Texas Intermediate crude oil price
ISSN:0306-2619, 1872-9118
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•We propose approach for the prediction of the WTI crude oil price.•The values predicted by the proposed method and actual once are statistically equal.•The proposed method indicated performance improvement over existing results. This paper proposes an alternative approach based on a genetic algorithm and neural network (GA–NN) for the prediction of the West Texas Intermediate (WTI) crude oil price. Comparative simulation results suggested that the proposed GA–NN approach is better than the baseline algorithms in terms of prediction accuracy and computational efficiency. Mann–Whitney test results indicated that the WTI crude oil price predicted by the proposed GA–NN and the observed price are statistically equal. Further comparison of the proposed GA–NN with previous studies indicated performance improvement over existing results. The proposed model can be useful in the formulation of policies related to international crude oil price estimations, development plans and industrial production.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0306-2619
1872-9118
DOI:10.1016/j.apenergy.2014.12.045