Orthogonal wavelet support vector machine for predicting crude oil prices

Previous studies mainly used radial basis, sigmoid, polynomial, linear, and hyperbolic functions as the kernel function for computation in the neurons of conventional support vector machine (CSVM) whereas orthogonal wavelet requires less number of iterations to converge than these listed kernel func...

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Main Authors:	Chiroma, Haruna, Abdul-Kareem, Sameem, Abubakar, Adamau, Zeki, Akram M., Usman, Mohammed Joda
Format:	Conference or Workshop Item
Language:	English English
Published:	Springer 2014
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/36930/ http://irep.iium.edu.my/36930/ http://irep.iium.edu.my/36930/ http://irep.iium.edu.my/36930/4/Orthogonal_Wavelet_Support_Vector_Machine_for_Predicting_Crude_Oil_Prices%2B.pdf http://irep.iium.edu.my/36930/7/36930_Orthogonal%20wavelet%20support%20vector%20machine%20for%20predicting%20crude%20oil%20prices.SCOPUS.pdf

id	iium-36930
recordtype	eprints
spelling	iium-369302017-09-26T06:46:07Z http://irep.iium.edu.my/36930/ Orthogonal wavelet support vector machine for predicting crude oil prices Chiroma, Haruna Abdul-Kareem, Sameem Abubakar, Adamau Zeki, Akram M. Usman, Mohammed Joda T Technology (General) Previous studies mainly used radial basis, sigmoid, polynomial, linear, and hyperbolic functions as the kernel function for computation in the neurons of conventional support vector machine (CSVM) whereas orthogonal wavelet requires less number of iterations to converge than these listed kernel functions. We proposed an orthogonal wavelet support vector machine (OSVM) model for predicting the monthly prices of West Texas Intermediate crude oil prices. For evaluation purposes, we compared the performance of our results with that of the CSVM, and multilayer perceptron neural network (MLPNN). It was found to perform better than the CSVM, and the MLPNN. Moreover, the number of iterations, and time computational complexity of the OSVM model is less than that of CSVM, and MLPNN. Experimental results suggest that the OSVM is effective, robust, and can efficiently be used for crude oil price prediction. Our proposal has the potentials of advancing the prediction accuracy of crude oil prices, which makes it suitable for building intelligent decision support systems. Springer 2014 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/36930/4/Orthogonal_Wavelet_Support_Vector_Machine_for_Predicting_Crude_Oil_Prices%2B.pdf application/pdf en http://irep.iium.edu.my/36930/7/36930_Orthogonal%20wavelet%20support%20vector%20machine%20for%20predicting%20crude%20oil%20prices.SCOPUS.pdf Chiroma, Haruna and Abdul-Kareem, Sameem and Abubakar, Adamau and Zeki, Akram M. and Usman, Mohammed Joda (2014) Orthogonal wavelet support vector machine for predicting crude oil prices. In: 1st International Conference on Advanced Data and Information Engineering (DaEng 2013), 16th-18th Dec. 2013, Cititel Hotel, Mid Valley, Kuala Lumpur. http://link.springer.com/chapter/10.1007%2F978-981-4585-18-7_23 doi:10.1007/978-981-4585-18-7_23
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	T Technology (General)
spellingShingle	T Technology (General) Chiroma, Haruna Abdul-Kareem, Sameem Abubakar, Adamau Zeki, Akram M. Usman, Mohammed Joda Orthogonal wavelet support vector machine for predicting crude oil prices
description	Previous studies mainly used radial basis, sigmoid, polynomial, linear, and hyperbolic functions as the kernel function for computation in the neurons of conventional support vector machine (CSVM) whereas orthogonal wavelet requires less number of iterations to converge than these listed kernel functions. We proposed an orthogonal wavelet support vector machine (OSVM) model for predicting the monthly prices of West Texas Intermediate crude oil prices. For evaluation purposes, we compared the performance of our results with that of the CSVM, and multilayer perceptron neural network (MLPNN). It was found to perform better than the CSVM, and the MLPNN. Moreover, the number of iterations, and time computational complexity of the OSVM model is less than that of CSVM, and MLPNN. Experimental results suggest that the OSVM is effective, robust, and can efficiently be used for crude oil price prediction. Our proposal has the potentials of advancing the prediction accuracy of crude oil prices, which makes it suitable for building intelligent decision support systems.
format	Conference or Workshop Item
author	Chiroma, Haruna Abdul-Kareem, Sameem Abubakar, Adamau Zeki, Akram M. Usman, Mohammed Joda
author_facet	Chiroma, Haruna Abdul-Kareem, Sameem Abubakar, Adamau Zeki, Akram M. Usman, Mohammed Joda
author_sort	Chiroma, Haruna
title	Orthogonal wavelet support vector machine for predicting crude oil prices
title_short	Orthogonal wavelet support vector machine for predicting crude oil prices
title_full	Orthogonal wavelet support vector machine for predicting crude oil prices
title_fullStr	Orthogonal wavelet support vector machine for predicting crude oil prices
title_full_unstemmed	Orthogonal wavelet support vector machine for predicting crude oil prices
title_sort	orthogonal wavelet support vector machine for predicting crude oil prices
publisher	Springer
publishDate	2014
url	http://irep.iium.edu.my/36930/ http://irep.iium.edu.my/36930/ http://irep.iium.edu.my/36930/ http://irep.iium.edu.my/36930/4/Orthogonal_Wavelet_Support_Vector_Machine_for_Predicting_Crude_Oil_Prices%2B.pdf http://irep.iium.edu.my/36930/7/36930_Orthogonal%20wavelet%20support%20vector%20machine%20for%20predicting%20crude%20oil%20prices.SCOPUS.pdf
first_indexed	2023-09-18T20:52:58Z
last_indexed	2023-09-18T20:52:58Z
_version_	1777410117143101440

Orthogonal wavelet support vector machine for predicting crude oil prices

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