… traffic matrix prediction, in this paper we apply and evaluate the effectiveness of various
RNN networks on GEANT backbone networks. … of RNN networks and traffic matrix prediction. …

… , and Gated Recurrent Units (GRU)) to solve the network traffic prediction … in network traffic
prediction: volume prediction, packet protocol prediction, and packet distribution prediction. …

… network approaches for computer network traffic … network (RNN); 4) deep learning stacked
autoencoder (SAE). The computer network traffic is sampled from the traffic of the network …

… In Section II we review some works related to the use of machine learning as a tool for network
traffic prediction, as well as the several machine learning algorithms specifically designed …

… core components of network traffic prediction model which plays an important role in network
management. This article discusses past network traffic prediction research and critically …

… for prediction of network traffic [26–28]. Deep-learning algorithms are used for traffic prediction
in wireless mesh networks in [29] where researchers propose a network traffic prediction …

… Prediction Model This section describes recurrent neural networks (RNN), which are used for
network traffic prediction, … of RNN, long short⁃term memory networks (LSTM). On the basis …

… (iv) Network Security. Among all these subfields, NTP focuses on analyzing the network
load and prediction of the network traffic to avoid faults and inefficiencies in networking. In this …

… section introduces work related to wireless network traffic prediction and then describes the
… , LA-ResNet, of wireless network traffic prediction proposed in this paper. The experimental …

… Network traffic prediction is important for network planning and routing configurations that …
This paper proposes a network traffic prediction method based on a deep belief network and a …