Forecasting Network Traffic: A Survey and Tutorial With Open-Source Comparative Evaluation

This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic. We examine works based on autoregressive moving average models, like ARMA, ARIMA and SARIMA, as well as works based on Artifical Neural Networks approaches, such as RNN, LSTM, GRU, and CNN. In all cases, we provide a complete and self-contained presentation of the mathematical foundations of each technique, which allows the reader to get a full understanding of the operation of the different proposed methods. Further, we perform numerical experiments based on real data sets, which allows comparing the various approaches directly in terms of fitting quality and computational costs. We make our code publicly available, so that readers can readily access a wide range of forecasting tools, and possibly use them as benchmarks for more advanced solutions.

Automated summary

This paper reviews the literature on network traffic prediction and serves as a tutorial on the topic. It analyzes works based on autoregressive moving average models and artificial neural networks approaches. The paper provides a complete presentation of the mathematical foundations of each technique and performs numerical experiments based on real data sets to compare the different approaches. The code is also made publicly available for readers to access a wide range of forecasting tools.