摘要
为了提高复杂多变的网络流量预测精度,提出了一种基于仿射传播聚类算法和稀疏贝叶斯的网络流量预测模型。采用仿射传播聚类算法对网络流量训练集进行聚类,从而将网络流量训练集划分为若干个子类,然后采用稀疏贝叶斯回归为每个子类建立相应的预测模型,最后采用具体的网络流量数据对模型的性能进行测试。实验结果表明,模型可以获得比较理想的网络流量预测结果,预测误差可以满足网络流量的实际应用要求。
In order to improve the prediction accuracy of complex network traffic, this paper proposed a novel network traffic prediction model based on affinity propagation clustering algorithm and sparse Bayesian. Firstly, it used the affinity propagation clustering algorithm to cluster the network traffic training set, to divided the network traffic training set into several sub catego-ries. Then it used the sparse Bayesian regression to establish prediction models for each sub categories. Finally it tested the performance of network traffic prediction model on specific network traffic data to the model. The experimental results show that the proposed model can obtain more ideal predict results of network traffic, the prediction error can satisfy the practical application requirement of network flow.
出处
《计算机应用研究》
CSCD
北大核心
2015年第11期3371-3374,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(61103017)
连云港市社会发展项目(SH1212)
连云港市科技公关项目(CG1215)
关键词
网络流量
仿射传播
稀疏贝叶斯模型
组合预测
network traffic
affinity propagation
sparse Bayesian model
combination prediction
