An Application of Internet Traffic Prediction with Deep Neural Network

The advance knowledge of future traffic load is helpful for network service providers to optimize the network resource and to recover the demand criteria. This paper presents the task of internet traffic prediction with three different architectures of Deep Belief Network (DBN). The artificial neural network is created with the depth of 4 hidden layers in each model to learn the nonlinear hierarchal essence present in the time series of internet traffic data. The deep learning in the network is executed with unsupervised pretraining of the layers. The emphasis is given to the topology of DBN that achieves excellent prediction accuracy. The adopted approach provides accurate traffic predictions while simulating the traffic data patterns and stochastic elements, achieving 0.028 Root Mean Square Error (RMSE) value on the test dataset . To validate our choice for hidden layer size selection, further more experiments were done for chaotic time series prediction