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Towards traffic matrix prediction with LSTM recurrent neural networks

Towards traffic matrix prediction with LSTM recurrent neural networks

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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.

http://iet.metastore.ingenta.com/content/journals/10.1049/el.2018.0336
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