基于遗传算法优化支持向量机的网络流量预测

Internet Traffic Forecasting Based on Support Vector Machine Optimized by Genetic Algorithm

介绍了支持向量机用于时间序列预测的理论基础和遗传算法优化支持向量机参数的方法,首次把遗传算法优化参数支持向量机应用于两组实际网络流量的预测,并与BP神经网络和RBF神经网络方法进行了比较。结果表明:支持向量机相比较BP神经网络和RBF神经网络对网络流量的预测结果精度更高、性能更好。利用支持向量机预测网络流量是一种可行、有效的方法。

The basic theory of time series forecasting based on Support Vector Machine （SVM） is introduced in this paper. And Genetic Algorithm （GA） optimizes the parameters of SVM. GA-SVM is firstly applied to forecast future Internet traffic including two sets of real data, and compared to the BP and RBF neural network. The results show that SVM is superior to these two kinds of neural network methods in prediction performance. And SVM is the suitable and effective method for forecasting Internet traffic.