Internet traffic prediction with deep neural networks

With the evolution of Internet, traffic prediction has been more important than ever, because better resource allocation and network management schemes are based on the precise prediction of future demands. Formulated as a time series prediction problem, different solutions have been proposed, including linear statistical models and non-linear machine learning models. However, there lacks of a comprehensive evaluation of the recently developed deep neural networks for this important problem, which we aim to fill in this letter. Based on an open Internet bandwidth usage dataset collected for 6 months, 13 deep neural networks are evaluated and compared with five baseline models. The experiments demonstrate that all deep neural networks outperform baseline models, in particular among them InceptionTime achieves the lowest prediction error, in terms of RMSE and MAE. As a benchmark for future studies, the dataset, code, and results are publicly available in a Github repository.