A Network Traffic Prediction Method Based on LSTM
WANG Shihao, ZHUO Qinzheng, YAN Han, LI Qianmu, and QI Yong
( School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China)
As the sizes of networks continue to increase, network traffic grows exponentially. In this situation, how to accurately predict network traffic to serve customers better has become one of the issues that Internet service providers care most about. Current traditional network models cannot predict network traffic that behaves as a nonlinear system. In this paper, a long short-term memory (LSTM) neural network model is proposed to predict network traffic that behaves as a nonlinear system. According to characteristics of autocorrelation, an autocorrelation coefficient is added to the model to improve the accuracy of the prediction model. Several experiments were conducted using real-world data, showing the effectiveness of LSTM model and the accuracy that were improved with autocorrelation considered. The experimental results show that the proposed model is efficient and suitable for real-world network traffic prediction.
recurrent neural networks; time series; network traffic prediction