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 redictions. Consequently, the estimation of fuzzy upper and lower bounds become an essential issue in redicting 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.