Analyzing foreign exchange rates by rough set theory and directed acyclic graph support vector machines

Rough set theory (RST) and directed acyclic graph support vector machines (DAGSVM) are two emerging techniques in dealing with classification problems. The RST approach is able to select important features and generate rules from data. The SVM technique is powerful in solving classification problems with high generalization ability by applying the structure risk minimization principle. However, one particular model cannot capture all data patterns easily. This investigation presents a hybrid RST and DAGSVM model (RSTDAGSVM) to exploit the unique strengths of both RST and SVM in analyzing the movements of exchange rates. In the proposed hybrid model, the RST approach is used to extract the rules of exchange rate changes; and the DAGSVM technique is employed to deal with situations that cannot be included in the RST model. In addition, an immune algorithm and tabu search (IA/TS) method is applied to select parameters of SVM models. Experimental results reveal that the developed model achieves more accurate prediction results than either the RST model or the DAGSVM model on its own. Thus, the presented RSTDAGSVM model is a promising alternative for analyzing exchange rates.