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针对网络流量的预测问题,结合网络流量序列的自相似性分析而提出一种基于差分自回归滑动平均模型(ARIMA)补偿极限学习机(ELM)的网络流量预测方法.首先利用ELM模型对网络流量序列进行一步预测,然后对网络流量预测的误差序列通过ARIMA模型进行修正,最后将ELM模型预测值与ARIMA模型修正值进行叠加得到最终的预测值.与单独的ARIMA模型、最小二乘支持向量机(LS-SVM)预测模型以及Elman神经网络预测模型进行了对比,仿真结果表明本文的方法具有更高的预测精度.
Aiming at the problem of network traffic prediction, this paper proposes a network traffic prediction method based on the self-similarity analysis of network traffic sequence based on the differential self-regressive moving average (ARIMA) compensation limit learning machine (ELM) Then the error sequence of network traffic prediction is corrected by ARIMA model, and finally the prediction value of ELM model and ARIMA model correction value are superposed to obtain the final prediction value.Compared with the ARIMA model alone, least squares support vector machine (LS-SVM) prediction model and Elman neural network prediction model are compared. The simulation results show that the proposed method has higher prediction accuracy.