Third IEEE International Conference on Data Mining (2003)
Nov. 19, 2003 to Nov. 22, 2003
Joarder Kamruzzaman , Monash University, Victoria, Australia
Ruhul A Sarker , University of NSW, Canberra, Australia
Iftekhar Ahmad , Monash University, Victoria, Australia
Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e.g., neural network or ARIMA based model. SVM-based forecasting model necessitates the selection of appropriate kernel function and values of free parameters: regularization parameter and \varepsilon- insensitive loss function. In this paper, we investigate the effect of different kernel functions, namely, linear, polynomial, radial basis and spline on prediction error measured by several widely used performance metrics. The effect of regularization parameter is also studied. The prediction of six different foreign currency exchange rates against Australian dollar has been performed and analyzed. Some interesting results are presented.
I. Ahmad, R. A. Sarker and J. Kamruzzaman, "SVM Based Models for Predicting Foreign Currency Exchange Rates," Third IEEE International Conference on Data Mining(ICDM), Melbourne, Florida, 2003, pp. 557.