Gasoline price forecasting: An application of LSSVM with improved ABC

• Main Authors: Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira
• Format: Conference or Workshop Item
• Language: English
• Published: Elsevier Ltd. 2014
• Subjects: QA76 Computer software
• Online Access: http://repo.uum.edu.my/14831/1/1-s2.0RG.pdf; http://repo.uum.edu.my/14831/; http://doi.org/10.1016/j.sbspro.2014.03.718
• Institution: Universiti Utara Malaysia

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.