The nature of statistical learning theory
The nature of statistical learning theory
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
A new approach to fuzzy regression models with application to business cycle analysis
Fuzzy Sets and Systems
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
Fuzzy Regression Analysis by Support Vector Learning Approach
IEEE Transactions on Fuzzy Systems
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
Information Sciences: an International Journal
Applying case based reasoning for prioritizing areas of business management
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Business cycle predictions face various sources of uncertainty and imprecision. The uncertainty is usually linguistically determined by the beliefs of decision makers. Thus, the fuzzy set theory is ideally suited to depict vague and uncertain features of business cycle predictions. Consequently, the estimation of fuzzy upper and lower bounds become an essential issue in predicting business cycles in an uncertain environment. The support vector regression (SVR) model is a novel forecasting approach that has been successfully used to solve time series problems. However, the SVR approach has not been widely applied in fuzzy forecasting problems. This study employs support vector regressions to calculate fuzzy upper and lower bounds; and presents a fuzzy support vector regression (FSVR) model for forecasting indices of business cycles. A numerical example of a business cycle prediction in Taiwan was used to demonstrate the forecasting performance of the FSVR model. The empirical results are satisfactory. Therefore, the FSVR model is an effective alternative in forecasting business cycles under uncertain circumstances.