Combination Optimization Research on Grey Model and Support Vector Machine

Grey model and support vector machine are fit for prediction in the small size of data, their advantages and disadvantages are probed in this paper at first. And then, the combined model is proposed, which combines grey model and support vector machine with optimal weights. The weights are obtained and optimized by minimizing the sum of squared residuals standard. Some experiments compared with grey model and support vector machine are done, and the experimental results show that the combined model proposed are not only more effective and reliable, but also can further improve the precision prediction.