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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kuo-Ping Lin Kuo-Chen Hung Ming-Chang Wu |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Information Management, Lunghwa University of Science and Technology, Taoyuan, Taiwan (Kuo-Ping Lin; Ming-Chang Wu) || Department of Logistics Management, National Defense University, Taipei, Taiwan (Kuo-Chen Hung) |
| Abstract | Long-term business cycle forecasting is a very important issue in economic evaluation. This study presents a novel intuitionistic fuzzy least-squares support vector regression (IFLS-SVR) model for accurately forecasting long-term index of business cycle. Traditional support vector regression (SVR) and least-squares support vector regressions (LS-SVR) have been successfully applied in forecasting problems especially complex/nonlinear system. In this paper, the prediction model adopts two least-squares support vector regressions with intuitionistic fuzzy sets to approach fuzzy upper and lower bounds respectively, and then approach the crisp predict values. Genetic algorithms (GAs) are also employed in order to select two parameters of IFLS-SVR models. In this study, IFLS-SVR, LS-SVR and SVR are employed for the Taiwan business index forecasting. Empirical results indicate that the IFLS-SVR has better performance in terms of forecasting accuracy. Therefore, the IFLS-SVR model can efficiently provide credible long-term prediction for the Taiwan business index forecasting |
| Starting Page | 2495 |
| Ending Page | 2499 |
| File Size | 229684 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007546 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Forecasting Business Support vector machines Predictive models Fuzzy sets Indexes Genetic algorithms least-squares support vector regressions long-term business cycle forecasting intuitionistic fuzzy support vector regression support vector regression |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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