Training Set of Support Vector Regression Extracted by Empirical Mode Decomposition

Support vector regression (SVR) is a common learning method for machines which is developed these years. Comparing with the other regression models, this method has the advantages of structural risk minimization and strong generalization ability. It is widely used in the prediction field and acquires good effects. The training characters of SVR model are very important to SVR. To solve the problem, this paper puts forward a method of SVR training by the characters which extracted by empirical mode decomposition (EMD). This method firstly uses the EMD to decompose the signal to get the intrinsic mode function (IMF), and then uses the components data of each time spot as features to train SVR. Meanwhile, the forecasting model is obtained. This method is used to forecast the wind speed. The experiment shows that the method improves the calculating precision greatly, increases the number of effective forecasting points, and has the self-adoptive characteristic.