Application of Online Selection Support Vector Classification in the Prediction of Ups and Downs in Stock Market

Support Vector Machine(SVM) which is a new technology used in Data Mining.It is a new tool that accounts for the problems of the Machine Learning by the method of the optimization.Applying the support vector machine method in the research on the non-linear time series economic prediction problem is underway.It is more feasible and predominant than the Neural Networks algorithm in the extending ability and the tallying precision.After we studied the characteristics of the stock data and the rules of the stock market people,we put forward to a dynamic model which bases on the traditional support vector machine arithmetic.The model selects the training data online when we get the new data and then we modify the model each time base on the increased data in the aggregate.It is a dynamic model,so it can catch the real time change of the market.It make the prediction precision be improved comes to truth with the small workload as the cost.In this paper we use the support vector machine and the Time series dynamic model(DMDI) to predict the short-time and the medium-term ups and downs in the single stock and the holistic Shanghai stock market.We perform a large numbers of numerical experiments and compared with the results being got based on the methods of the BP neural networks and the static models which is not changed when the new data is got with the time going,and the prediction rightness probability is higher,and it is more feasible in the extending ability and the tallying precision through the actual application.In addition,It can also avoid the difficult problem——study of the training data excessively.The results show that the DMDI is more suitable for the forecasting the index time series of the stock market than the BP neural networks and the static models.The model we have proposed in this paper has more advantages in the prediction of the trends of the stock market than the conventional methods.