In the process of controlling network congestion and network management, how to accurately predict network traffic is very important. A network traffic prediction method based on autoregressive integrated moving average (ARIMA) model and adaptive Volterra filter model is proposed in this study. Firstly, ARIMA model is utilized to capture and predict the linear features of network traffic time series. The residual sequence is generated by network traffic sequence minus ARIMA predictive value sequence. The residual sequence only contains the nonlinear characteristics of original network traffic. Then an adaptive Volterra filter is used to train and predict the nonlinear residual sequence of original network traffic. The optimal parameters of adaptive Volterra filter are obtained by improved particle swarm optimization algorithm. Add the linear value predicted by ARIMA and the nonlinear value predicted by adaptive Volterra filter to get the final predicted value. The simulation results show that the proposed prediction method has high accuracy, small prediction error, and high reliability.

中文翻译：

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基于自回归综合移动平均和自适应Volterra滤波器的网络流量预测方法

在控制网络拥塞和网络管理的过程中，如何准确预测网络流量非常重要。本文提出了一种基于自回归综合移动平均(ARIMA)模型和自适应Volterra滤波器模型的网络流量预测方法。首先，利用ARIMA模型捕捉和预测网络流量时间序列的线性特征。残差序列由网络流量序列减去ARIMA预测值序列生成。残差序列只包含原始网络流量的非线性特征。然后使用自适应 Volterra 滤波器来训练和预测原始网络流量的非线性残差序列。自适应Volterra滤波器的最优参数是通过改进粒子群优化算法得到的。将 ARIMA 预测的线性值和自适应 Volterra 滤波器预测的非线性值相加，得到最终的预测值。仿真结果表明，所提出的预测方法准确率高、预测误差小、可靠性高。