摘要
为了准确预测短时间内的网络流量波动,提高95598客服热线网络流量监测性能,文章基于循环神经网络的GRU模型与后向传播神经网络,输入多种流量相关的影响因素,综合构建小尺度网络流量预测的深层网络模型。实验结果表明,与传统浅层神经网络预测模型和深层网络LSTM预测模型相比,文章所提的方法不但在短期流量预测取得更高的准确率,也在较长时序的流量预测上获得更好结果。
In order to accurately predict the fluctuation of network traffic in a short period of time and improve the monitoring performance of 95598 customer service hotline network traffic,considering multi-scale related traffic features, this paper proposes a small-scale network traffic prediction model based on RNN deep network model and BP neural network. The experimental results show that the proposed model achieves better prediction performance not only in short-term network traffic but also in long term traffic prediction than that of traditional neural networks.
引文
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