Towards traffic matrix prediction with LSTM recurrent neural networks. Issue 9 (1st May 2018) - Zhao, Jianlong
Abstract : This Letter investigates traffic matrix (TM) prediction that is widely used in various network management tasks. To fastly and accurately attain timely TM estimation in large‐scale networks, the authors propose a deep architecture based on LSTM recurrent neural networks (RNNs) to model the spatio‐temporal features of network traffic and then propose a novel TM prediction approach based on deep LSTM RNNs and a linear regression model. By training and validating it on real‐world data from Abilene network, the authors show that the proposed TM prediction approach can achieve state‐of‐the‐art TM prediction performance.