Model-free forecasting for nonlinear time series (with application to exchange rates)
Computational Statistics & Data Analysis
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Pairwise classification and support vector machines
Advances in kernel methods
Information Sciences—Informatics and Computer Science: An International Journal
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
Statistical fuzzy interval neural networks for currency exchange rate time series prediction
Applied Soft Computing
Color image segmentation: Rough-set theoretic approach
Pattern Recognition Letters
Automated extraction of decision rules for leptin dynamics-A rough sets approach
Journal of Biomedical Informatics
New robust forecasting models for exchange rates prediction
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
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.