经验模式分解与时间序列分析在网络流量预测中的应用

提出一种经验模式分解和时间序列分析的网络流量预测方法. 首先,对网络流量时间序列进行经验模式分解,产生高低频分量和余量;然后,对各分量进行时间序列分析,确保高频分量采用改进和声搜索算法优化的最小二乘支持向量机模型、低频分量和余量采用差分自回归滑动平均模型进行建模和预测;最后,将预测结果通过RBF神经网络进行非线性叠加,得到最终的预测值.仿真实验表明,所提出方法具有更好的预测效果和更高的预测精度.

Network traffic prediction based on empirical mode decomposition and time series analysis

A network traffic prediction method based on empirical mode decomposition and time series self-similar analysis is proposed. Firstly, network traffic time-series high and low frequency components are generated by empirical mode decomposition. Then the component time series is analyzed to determine that the least squares support vector machine model optimized by using the improved harmony search algorithm is used for high frequency components modeling, and the auto regressive integrated moving average model is used for low frequency components modeling and remaining component modeling. Finally, the final prediction result is obtained by RBF neural network nonlinear superposition. Simulation results show that the proposed method has better prediction results and higher prediction accuracy.