Forecasting Regional Economic Tendency Using Rough Sets-Based Support Vector Machines
Bangzhu Zhu · Jisuanji fangzhen · 2008
In order to resolve the problem of how to select the variables for support vector machines (SVM) of regional economic forecasting, a hybrid forecasting method, rough sets based support vector machines (RSSVM) model, is presented for forecasting regional economic tendency. In this hybrid approach, rough sets (RS) are used for variable selection in order to reduce the model complexity of support vector machines (SVM ) and improve the speed of SVM, and then the SVM is used to identify regional economic movement direction based on the historical data. The empirical results reveal that RSSVM method has understanding forecasting ability. Compared with the standard SVM, RSSVM has great superiority in predicting accuracy.