基于大数据分析的非线性网络流量组合预测模型

Combined prediction model for nonlinear network flow based on big data analysis

针对传统方法不能对网络流量变化特征进行准确描述,并且预测精度较低的问题,提出了基于大数据分析的非线性网络流量组合预测模型.通过对非线性网络流量数据进行有效分解,获得不同尺度的分量,利用混沌理论对多尺度分量进行相空间重构获得流量子序列.构建改进鸟群算法优化模型,并对重构后的网络流量子序列进行预测和组合,获得网络流量预测结果.结果表明,所提模型能够精确地描述网络流量的非线性、周期性以及长相关性等变化特征,具有较高的预测精度.

In order to solve the problem that traditional methods can not accurately describe the changing characteristics of network flow and have low prediction accuracy,a combined prediction model for nonlinear network flow based on large data analysis was proposed.Through the effective decomposition of nonlinear network flow data,the components with different scales were obtained.The flow subsequences were obtained through the phase-space reconstruction of components with different scales in terms of chaos theory.An improved bird swarm optimization model was constructed,the reconstructed network flow subsequence was predicted and combined,and the results of prediction model for network flow were attained.The results show that the as-proposed model can accurately describe the non-linear,periodic and long-term correlation characteristics of network flow,and has higher prediction accuracy.