The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine

Aiming at the complex system with multi-dimension, small samples, nonlinearity and multi-apex, and combining chaos theory, genetic algorithm with support vector machine (SVM), a kind of chaotic SVM named Cv-SVM short for chaotic v-support vector machine is proposed in this paper. Cv-SVM, whose constraint conditions are less than those of the standard v-SVM by one, is proved to satisfy the structure risk minimum rule under the condition of probability. Moreover, there is no parameter b in the regression function of Cv-SVM. And then, an intelligence-forecasting method is put forward. The results of application in car demand forecasting show that the forecasting method based on Cv-SVM is feasible and effective.