A Hybrid Support Vector Machine Regression for Exchange Rate Prediction

摘要

Support vector machines (SVMs) have been successfully used to solve nonlinear regression and times series problems. Unlike most conventional neural network models, which are based on the empirical risk minimization principle, SVMs apply the structural risk minimization principle to minimize an upper bound of the generalization error, rather than minimizing the training error. However, one particular model can not capture all data patterns easily. This investigation presents a hybrid SVM model to exploit the unique strength of the linear and nonlinear SVM models in forecasting exchange rate. Furthermore, parameters of both the linear and nonlinear SVM models are determined by Genetic Algorithms (GAs). A numerical example from an existing literature is employed to compare the performance of the proposed model. Experiment results show that the proposed model outperforms the other approaches in the literature.