Long-term business cycle forecasting using intuitionistic fuzzy least-squares support vector regression

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.