Research Article Open Access

A Hybridization of Enhanced Artificial Bee Colony-Least Squares Support Vector Machines for Price Forecasting

Zuriani Mustaffa1 and Yuhanis Yusof1
  • 1 University Utara Malaysia, Malaysia

Abstract

Problem statement: As the performance of Least Squares Support Vector Machines (LSSVM) is highly rely on its value of regularization parameter, γ and kernel parameter, σ2, man-made approach is clearly not an appropriate solution since it may lead to blindness in certain extent. In addition, this technique is time consuming and unsystematic, which consequently affect the generalization performance of LSSVM. Approach: This study presents an enhanced Artificial Bee Colony (ABC) to automatically optimize the hyper parameters of interest. The enhancement involved modifications that provide better exploitation activity by the bees during searching and prevent premature convergence. Later, the prediction process is accomplished by LSSVM. Results and Conclusion: Empirical results obtained indicated that feasibility of proposed technique showed a satisfactory performance by producing better prediction accuracy as compared to standard ABC-LSSVM and Back Propagation Neural Network.

Journal of Computer Science
Volume 8 No. 10, 2012, 1680-1690

DOI: https://doi.org/10.3844/jcssp.2012.1680.1690

Submitted On: 25 June 2012 Published On: 28 August 2012

How to Cite: Mustaffa, Z. & Yusof, Y. (2012). A Hybridization of Enhanced Artificial Bee Colony-Least Squares Support Vector Machines for Price Forecasting. Journal of Computer Science, 8(10), 1680-1690. https://doi.org/10.3844/jcssp.2012.1680.1690

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Keywords

  • Artificial Bee Colony (ABC)
  • energy fuel price time series
  • financial prediction
  • Least Squares Support Vector Machines (LSSVM)