Abstract—Reliable forecasts of the price of natural resource commodity is of interest for a wide range of applications. This includes generating macroeconomic projections and in assessing macroeconomic risks. Various approaches have been introduced in developing the required forecasting models. In this paper, a forecasting model based on an optimized Least Squares Support Vector Machine is proposed. The determination of hyper-parameters is performed using a nature inspired algorithm, Artificial Bee Colony. The proposed forecasting model is realized in gold price forecasting. The undertaken experiments showed that the prediction accuracy and Mean Absolute Percentage Error produced by the proposed model is better compared to the one produced using Least Squares Support Vector Machine as an individual.
Index Terms—Artificial bee colony, least squares support vector machine, swarm computing, forecasting, optimization.
Yuhanis Yusof, Siti Sakira Kamaruddin, Husniza Husni, Ku Ruhana Ku-Mahamud, and Zuriani Mustaffa are with the School of Computing, UUM College of Arts and Sciences, Universiti Utara Malaysia, 06010, Sintok, Kedah, Malaysia (e-mail: yuhanis@uum.edu.my, sakira@uum.edu.my, husniza@uum.edu.my, ruhana@uum.edu.my, zuriani.m@gmail.com).
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Cite:Yuhanis Yusof, Siti Sakira Kamaruddin, Husniza Husni, Ku Ruhana Ku-Mahamud, and Zuriani Mustaffa, "Forecasting Model Based on LSSVM and ABC for Natural Resource Commodity," International Journal of Computer Theory and Engineering vol. 5, no. 6, pp. 906-909, 2013.
