基于滚动窗法最小二乘支持向量机的稳健预测模型

Robust Prediction Model of Least Squares Support Vector Machine Based on Sliding Window

在推导加权最小二乘支持向量机数学模型的基础上,基于启发式学习算法并结合滚动窗的思想,提出基于滚动窗法最小二乘支持向量机的稳健预测模型.为了缩短模型的预测运行时间,将启发式算法进行改进后,采用迭代求逆方法,在不丧失预测精度的基础上,很大程度地缩短预测时间.最后通过仿真实例验证这个模型可以成功抑制奇异点,实现稳健预测并取得良好效果.

In this paper, the mathematical model of weighted least squares support vector machine （WLS-SVM） is introduced. Based on the algorithms of heuristic learning and sliding window, a mathematical model of robust prediction of least squares support vector machine （LS-SVM） using sliding window is proposed, with the modified heuristic learning algorithm, the strategy of iterative computing matrix inverse is employed to reduce the predicted time without loss of accuracy. Finally, two examples have proved that the proposed model can eliminate the outliers, realize robust prediction and achieve good results.