GAN vs. LSTM: Network State Prediction

Abstract

Network Traffic prediction is the prerequisite for proactive traffic management, where a longer duration and high accuracy of prediction ensures a more effective solution. This paper exploits Generative Adversarial Network (GAN) architecture to propose a model that utilizes spatiotemporal features in network traffic data for extended prediction of future traffic. The generator in GAN consists of a Convolutional Long Short-Term Memory (ConvLSTM) model for predicting network traffic, while the discriminator uses Convolutional Neural Network (CNN) model that provides feedback to the generator for training. The experiments based on network traces show that the proposed model reduces the error by 12% compared to the baseline model while predicting next 48 mins of network traffic.