ROBUST V-SUPPORT VECTOR MACHINE AND ITS APPLICATION TO SALE FORECASTING

In view of the low generalization capacity of standard support vector machine for some types of noises existing in time series such as white noise, singularities, and biggish magnitude noises, a robust loss function is designed to inhibit (penalize) the above hybrid noises. The structure risk minimization (SRM) based on a geometrical interval with a special hyper-plane is theoretically proved. On the basis of that, a new support vector machine called R nu -SVM (robust nu -support vector machine) that meets SRM is proposed to handle sale series. Moreover, by modifying the standard nu -SVM formulation, R nu -SVM is characterized with its simpler dual optimization and fewer output parameters. With the help of the above, a sale forecasting method based on R nu -SVM and its relevant parameter-choosing algorithm is formulated and applied to car sales forecasting, the convincing results of which definitely confirm the feasibility and validity of the forecasting method. Compared with the standard nu -SVM and traditional model, the R nu -SVM method is of better estimating precision and higher generalization capacity.