Mustaffa, Zuriani and Yusof, Yuhanis and Kamaruddin, Siti Sakira (2014) Gasoline price forecasting: An application of LSSVM with improved ABC. In: 2nd International Conference on Innovation, Management and Technology Research, 22 – 23 September, 2013, Klana Resort, Negeri Sembilan, Malaysia.

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Abstract

Optimizing the hyper-parameters of Least Squares Support Vector Machines (LSSVM) is crucial as it will directly influence the predictive power of the algorithm.To tackle such issue, this study proposes an improved Artificial Bee Colony (IABC) algorithm which is based on conventional mutation.The IABC serves as an optimizer for LSSVM.Realized in gasoline price forecasting, the performance is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Percentage Error (RMSPE).The conducted simulation results show that, the proposed IABCLSSVM outperforms the results produced by ABC-LSSVM and also the Back Propagation Neural Network.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	Organised by Universiti Malaysia Kelantan
Uncontrolled Keywords:	Least Squares Support Vector Machines; Artificial Bee Colony; Price forecasting
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	School of Computing
Depositing User:	Dr. Yuhanis Yusof
Date Deposited:	14 Jul 2015 07:36
Last Modified:	23 May 2016 07:26
URI:	https://repo.uum.edu.my/id/eprint/14831

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