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

fYear

2014

fDate

24-25 March 2014

Firstpage

94

Lastpage

98

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Engineering and Optimization Conference (PEOCO), 2014 IEEE 8th International

Conference_Location

Langkawi

Print_ISBN

978-1-4799-2421-9

Type

conf

DOI

10.1109/PEOCO.2014.6814406

Filename

6814406