An intelligent forecasting model based on robust wavelet ν-support vector machine

Aiming at the problem of small samples, season character, nonlinearity, randomicity and fuzziness in product demand series, the existing support vector kernel does not approach the random curve of the demands time series in the L2(Rn) space (quadratic continuous integral space). The robust loss function is also proposed to solve the shortcoming of ε-insensitive loss function during handling hybrid noises. A novel robust wavelet support vector machine (RW ν-SVM) is proposed based on wavelet theory and the modified support vector machine. Particle swarm optimization algorithm is designed to select the optimal parameters of RW ν-SVM model in the scope of constraint permission. The results of application in car demand forecasts show that the forecasting approach based on the RW ν-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than RW ν-SVM and other traditional methods.