Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space

This paper presents a new version of fuzzy support vector machine to forecast multi-dimension time series. Since there exist some problems of finite samples and uncertain data in many forecasting problem, the input variables are described as real numbers by fuzzy comprehensive evaluation. To represent the fuzzy degree of these input variables, the symmetric triangular fuzzy technique is adopted. Then by combining the fuzzy theory with ν-support vector machine, the fuzzy ν-support vector machine (Fν-SVM) on the triangular fuzzy space is proposed. To seek the optimal parameters of Fν-SVM, particle swarm optimization is also proposed to optimize parameters of Fν-SVM. The results of the application in sale forecasts confirm the feasibility and the validity of the Fν-SVM model. Compared with the traditional model, Fν-SVM method requires fewer samples and has better forecasting precision.