SVM Based Models for Predicting Foreign Currency Exchange Rates

Authors:
Joarder Kamruzzaman;Ruhul A. Sarker;Iftekhar Ahmad
Affiliations:
-;-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

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Abstract

Support vector machine (SVM) has appeared as a powerfultool for forecasting forex market and demonstrated betterperformance over other methods, e.g., neural network orARIMA based model. SVM-based forecasting modelnecessitates the selection of appropriate kernel function andvalues of free parameters: regularization parameter and \varepsilon-insensitive loss function. In this paper, we investigate the effectof different kernel functions, namely, linear, polynomial, radialbasis and spline on prediction error measured by several widelyused performance metrics. The effect of regularizationparameter is also studied. The prediction of six different foreigncurrency exchange rates against Australian dollar has beenperformed and analyzed. Some interesting results are presented.