Orthogonal Wavelet Support Vector Machine for Predicting Crude Oil Prices

  • Haruna Chiroma
  • Sameem Abdul-Kareem
  • Adamau Abubakar
  • Akram M. Zeki
  • Mohammed Joda Usman
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)


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.


Support vector machine Orthogonal wavelet Crude oil prices Kernel function 


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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Haruna Chiroma
    • 1
  • Sameem Abdul-Kareem
    • 1
  • Adamau Abubakar
    • 2
  • Akram M. Zeki
    • 2
  • Mohammed Joda Usman
    • 3
  1. 1.Department of Artificial IntelligenceUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Computer Science, Faculty of Information and Communication TechnologyInternational Islamic University MalaysiaGombak, Kuala LumpurMalaysia
  3. 3.School of Electronic and Information EngineeringLiaoning University of TechnologyJinzhouChina

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