Application of LSSVM by ABC in energy commodity price forecasting

• Main Authors: Mustaffa, Zuriani, Yusof, Yuhanis, Kamaruddin, Siti Sakira
• Format: Conference or Workshop Item
• Language: English
• Published: 2014
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/20650/1/PEOCO%202014%2094%2098.pdf; http://repo.uum.edu.my/20650/; http://doi.org/10.1109/PEOCO.2014.6814406
• Institution: Universiti Utara Malaysia

The importance of the hyper parameters selection for a kernel-based algorithm, viz.Least Squares Support Vector Machines (LSSVM) has been a critical concern in literature.In order to meet the requirement, this work utilizes a variant of Artificial Bee Colony (known as mABC) for hyper parameters selection of LSSVM.The mABC contributes in the exploitation process of the artificial bees and is based on Levy mutation.Realized in crude oil price forecasting, the performance of mABC-LSSVM is guided based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSPE) and compared against the standard ABC-LSSVM and LSSVM optimized by Genetic Algorithm. Empirical results suggested that the mABC-LSSVM is superior than the chosen benchmark algorithms.