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首页> 外文期刊>International journal of communication systems >Network traffic prediction method based on wavelet transform and multiple models fusion
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Network traffic prediction method based on wavelet transform and multiple models fusion

机译：基于小波变换的网络流量预测方法和多模型融合

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摘要

Accurate prediction of network traffic is an important premise in network management and congestion control. In order to improve the prediction accuracy of network traffic, a prediction method based on wavelet transform and multiple models fusion is presented. Mallat wavelet transform algorithm is used to decompose and reconstruct the network traffic time series. The approximate and detailed components of the original network traffic can be obtained. The characteristics of approximate components and detail components are analyzed by Hurst exponent. Then, according to the different characteristics of the components, autoregressive integrated moving average model (ARIMA) is chosen as the prediction model for the approximate component. Least squares support vector machine (LSSVM) is used to predict detail component. Meanwhile, an improved particle swarm optimization (PSO) algorithm is proposed to optimize the parameters of the LSSVM model. Gauss-Markov estimation algorithm is adapted to fuse the predicted values of multiple prediction models. The variance of fusion prediction error is smaller than that of single prediction model, and the prediction accuracy is improved. Two actual datasets of network traffic are studied. Compared with other state-of-the-art models, the case study results indicate that the proposed prediction method has a better prediction effect.
机译：准确预测网络流量是网络管理和拥塞控制的重要前提。为了提高网络流量的预测精度，提出了一种基于小波变换和多模型融合的预测方法。 Mallat小波变换算法用于分解和重建网络流量时间序列。可以获得原始网络流量的近似和详细组件。赫斯特指数分析了近似组件和细节组件的特征。然后，根据组件的不同特征，选择自回归积分移动平均模型（ARIMA）作为近似分量的预测模型。最小二乘支持向量机（LSSVM）用于预测细节组件。同时，提出了一种改进的粒子群优化（PSO）算法来优化LSSVM模型的参数。高斯-Markov估计算法适于熔断多维预测模型的预测值。融合预测误差的方差小于单预测模型的方差，并且提高了预测精度。研究了两个网络流量的实际数据集。与其他最先进的模型相比，案例研究结果表明所提出的预测方法具有更好的预测效果。

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