Long-term business cycle forecasting through a potential intuitionistic fuzzy least-squares support vector regression approach

This paper developed a novel intuitionistic fuzzy least-squares support vector regression with genetic algorithms (IFLS-SVRGAs) to accurately forecast the long-term indexes of business cycles. Long-term business cycle forecasting is an important issue in economic evaluation, as business cycle indexes may contain uncertain factors or phenomena such as government policies and financial meltdowns. In order to effectively handle such factors and accidental forecasting indexes of business cycles, the proposed method combined intuitionistic fuzzy technology with least-squares support vector regression (LS-SVR). The LS-SVR method has been successfully applied to forecasting problems, especially time series problems. The prediction model in this paper adopted two LS-SVRs with intuitionistic fuzzy sets, in order to approach the intuitionistic fuzzy upper and lower bounds and to provide numeric prediction values. Furthermore, genetic algorithms (GAs) were simultaneously employed in order to select the parameters of the IFLS-SVR models. In this study, IFLS-SVRGA, intuitionistic fuzzy support vector regression (IFSVR), fuzzy support vector regression (FSVR), least-squares support vector regression (LS-SVR), support vector regression (SVR) and the autoregressive integrated moving average (ARIMA) were employed for the long-term index forecasting of Taiwanese businesses. The empirical results indicated. that the proposed IFLS-SVRGA model has better performance in terms of forecasting accuracy than the other methods. Therefore, the IFLS-SVRGA model can efficiently provide credible long-term predictions for business index forecasting in Taiwan. (C) 2012 Elsevier Inc. All rights reserved.