Modeling and Forecasting Method Based on Support Vector Regression

In the predicting financial distress, we know that irrelevant or correlated features in the samples could spoil the performance of the SVR classifier, leading to decrease of prediction accuracy. In order to solve the problems mentioned above, this paper use rough sets as a preprocessor of SVR to select a subset of input variables and employ the particle swarm optimization algorithm (PSOA) to optimize the parameters of SVR. The proposed PSOA-SVR model can automatically determine the optimal parameters. This model is tested on the prediction of financial distress. Then, we compare the proposed PSOA -SVR model with other artificial intelligence models of (BPN and fix-SVR). The experiment indicates that the proposed method is quite effective and ubiquitous.