Title :
Application of LSSVM by ABC in energy commodity price forecasting
Author :
Mustaffa, Z. ; Yusof, Y. ; Kamaruddin, S.S.
Author_Institution :
Sch. of Comput., Univ. Utara Malaysia, Sintok, Malaysia
Abstract :
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.
Keywords :
crude oil; genetic algorithms; least squares approximations; load forecasting; mean square error methods; power engineering computing; support vector machines; Levy mutation; MAPE; RMSPE; artificial bee colony; crude oil; energy commodity price forecasting; genetic algorithm; hyper parameters selection; kernel-based algorithm; least squares support vector machines; mABC-LSSVM; mean absolute percentage error; root mean square error; Conferences; Forecasting; Genetic algorithms; Optimization; Particle swarm optimization; Prediction algorithms; Support vector machines;
Conference_Titel :
Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International
Conference_Location :
Langkawi
Print_ISBN :
978-1-4799-2421-9
DOI :
10.1109/PEOCO.2014.6814406
