research-article

Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices

Authors:

Haruna Chiroma,

Abdulsalam Ya'u Gital,

Adamu Abubakar,

Mohammed Joda Usman,

Usman WaziriAuthors Info & Claims

CF '14: Proceedings of the 11th ACM Conference on Computing Frontiers

Article No.: 27, Pages 1 - 2

https://doi.org/10.1145/2597917.2597956

Published: 20 May 2014 Publication History

Get Access

Abstract

This study investigated the prediction of crude oil price based on energy product prices using genetically optimized Neural Network (GANN). It was found from experimental evidence that the international crude oil price can be predicted based on energy product prices. The comparison of the prediction performance accuracy of the propose GANN with Support Vector Machine (SVM), Vector Autoregression (VAR), and Feed Forward NN (FFNN) suggested that the propose GANN was more accurate than the SVM, VAR, and FFNN in the prediction accuracy and time computational complexity. The propose GANN was able to improve the performance accuracy of the comparison algorithms. Our approach can easily be modified for the prediction of similar commodities.

References

[1]

Hamilton, J. D. 2011. Historical oil shocks. In: Handbook of Major Events in Economic History, forthcoming.

Google Scholar

[2]

Zhang, Y. 2013. Speculative trading and WTI crude oil future price movement: An empirical analysis. Appl. Energ. 107, 394--402.

Crossref

Google Scholar

[3]

Chiroma, H., Abdul-Kareem, S. et al. 2014. Orthogonal Wavelet Support Vector Machine for Predicting Crude Oil Prices, Lec. Notes Elec. Eng. 285, 193--201.

Crossref

Google Scholar

[4]

Chiroma, H., Abdulkareem, S. Abubakar, A., and Usman, M. J. 2013. Computational Intelligence Techniques with Application to Crude Oil Price Projection: A Literature Survey from 2001--2012, Neural Netw. World, 23, 523--551.

Crossref

Google Scholar

[5]

Chevillon, G., and Rifflart, C. 2009. Physical market determinants of the price of crude oil and the market premium. Energy Econ. 31, 537--549.

Crossref

Google Scholar

[6]

Malliaris, M. E. and Malliaris, S. G. 2008. Forecasting energy product prices. Eur. J. Financ., 14,6, 453--468.

Crossref

Google Scholar

[7]

Samanta, B. 2004. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mechanical Systems and Signal Process, 18, 625--44.

Crossref

Google Scholar

Cited By

View all

Ahmad IIlyas HHussain SRaja M(2023)Evolutionary Techniques for the Solution of Bio-Heat Equation Arising in Human Dermal Region ModelArabian Journal for Science and Engineering10.1007/s13369-023-07907-5Online publication date: 23-May-2023
https://doi.org/10.1007/s13369-023-07907-5
Tiwari APant M(2022)Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3546194Online publication date: 30-Jun-2022
https://doi.org/10.1145/3546194
Drachal KPawłowski M(2021)A Review of the Applications of Genetic Algorithms to Forecasting Prices of CommoditiesEconomies10.3390/economies90100069:1(6)Online publication date: 19-Jan-2021
https://doi.org/10.3390/economies9010006
Show More Cited By

Index Terms

Optimization of neural network through genetic algorithm searches for the prediction of international crude oil price based on energy products prices

Recommendations

Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models

Given the importance of crude oil prices for businesses, governments and policy makers, this paper investigates predictability of oil prices using artificial neural networks taking into account the exhaustible nature of crude oil and impact of monetary ...
Passenger Capacity Prediction Based on Genetic Neural Network
IEEC '09: Proceedings of the 2009 International Symposium on Information Engineering and Electronic Commerce

The paper presents a genetic neural network model based on the features of genetic algorithm and artificial neural network. It was applied to predict passenger capacity of china. The forecast result shows that Genetic neural network model has a smaller ...
Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting

This paper investigates the effectiveness of the genetic algorithm (GA) evolved neural network for rainfall-runoff forecasting and its application to predict the runoff in a catchment located in a semi-arid climate in Morocco. To predict the runoff at ...

Comments

Information & Contributors

Information

Published In

CF '14: Proceedings of the 11th ACM Conference on Computing Frontiers

May 2014

305 pages

ISBN:9781450328708

DOI:10.1145/2597917

General Chair:
Pedro Trancoso
University of Cyprus, CY
,
Program Chairs:
Diana Franklin
University of California at Santa Barbara
,
Sally A. McKee
Chalmers University of Technology, SE

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 May 2014

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CF'14

Sponsor:

SIGMICRO

CF'14: Computing Frontiers Conference

May 20 - 22, 2014

Cagliari, Italy

Acceptance Rates

CF '14 Paper Acceptance Rate 28 of 62 submissions, 45%;

Overall Acceptance Rate 273 of 785 submissions, 35%

Upcoming Conference

CF '25

Sponsor:
sigmicro

22nd ACM International Conference on Computing Frontiers

May 28 - 30, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
176
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Ahmad IIlyas HHussain SRaja M(2023)Evolutionary Techniques for the Solution of Bio-Heat Equation Arising in Human Dermal Region ModelArabian Journal for Science and Engineering10.1007/s13369-023-07907-5Online publication date: 23-May-2023
https://doi.org/10.1007/s13369-023-07907-5
Tiwari APant M(2022)Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT NetworkACM Transactions on Multimedia Computing, Communications, and Applications10.1145/3546194Online publication date: 30-Jun-2022
https://doi.org/10.1145/3546194
Drachal KPawłowski M(2021)A Review of the Applications of Genetic Algorithms to Forecasting Prices of CommoditiesEconomies10.3390/economies90100069:1(6)Online publication date: 19-Jan-2021
https://doi.org/10.3390/economies9010006
Khan AShah RBukhari JAkhter NAttaullah Idrees MAhmad H(2019)A Novel Chicken Swarm Neural Network Model for Crude Oil Price PredictionAdvances on Computational Intelligence in Energy10.1007/978-3-319-69889-2_3(39-58)Online publication date: 13-Jul-2019
https://doi.org/10.1007/978-3-319-69889-2_3
Raja MShah FAlaidarous ESyam M(2017)Design of bio-inspired heuristic technique integrated with interior-point algorithm to analyze the dynamics of heartbeat modelApplied Soft Computing10.1016/j.asoc.2016.10.00952:C(605-629)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.asoc.2016.10.009
Cao JCui HShi HJiao L(2016)Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReducePLOS ONE10.1371/journal.pone.015755111:6(e0157551)Online publication date: 15-Jun-2016
https://doi.org/10.1371/journal.pone.0157551
Raja MZameer AKhan AWazwaz A(2016)A new numerical approach to solve Thomas–Fermi model of an atom using bio-inspired heuristics integrated with sequential quadratic programmingSpringerPlus10.1186/s40064-016-3093-55:1Online publication date: 23-Aug-2016
https://doi.org/10.1186/s40064-016-3093-5
Chiroma HAbdul-Kareem SAbdullahi Muaz SKhan ASari EHerawan T(2014)Neural Network Intelligent Learning Algorithm for Inter-related Energy Products ApplicationsAdvances in Swarm Intelligence10.1007/978-3-319-11857-4_32(284-293)Online publication date: 2014
https://doi.org/10.1007/978-3-319-11857-4_32

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models

Passenger Capacity Prediction Based on Genetic Neural Network

Evolving neural network using real coded genetic algorithm for daily rainfall-runoff forecasting

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Upcoming Conference

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations