### Survey of Network Traffic Forecast Based on Deep Learning

KANG Mengxuan, SONG Junping, FAN Pengfei, GAO Bowen, ZHOU Xu, LI Zhuo

1. 1.Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.China United Network Communications Co., Ltd., Beijing Branch, Beijing 100038, China
• Online:2021-05-15 Published:2021-05-10

### 基于深度学习的网络流量预测研究综述

1. 1.中国科学院 计算机网络信息中心，北京 100190
2.中国科学院大学，北京 100049
3.中国联合网络通信有限公司 北京市分公司，北京 100038

Abstract:

Precisely predicting the trend of network traffic changes can help operators accurately predict network usage, correctly allocate and efficiently use network resources to meet the growing and diverse user needs. Taking the progress of deep learning algorithms in the field of network traffic prediction as a clue, this paper firstly elaborates the evaluation indicators of network traffic prediction and the current public network traffic data sets. Secondly, this paper specifically analyzes four deep learning methods commonly used in network traffic prediction：deep belief networks, convolutional neural network, recurrent neural network, and long short term memory network, and focuses on the integrated neural network models used in recent years for different problems. The characteristics and application scenarios of each model are summarized. Finally, the future development of network traffic forecast is prospected.