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Article summary:
1. This paper presents a review of the literature on network traffic prediction, while also serving as a tutorial to the topic.
2. It examines 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.
3. Numerical experiments are performed with real-world citywide datasets of mobile network traffic to compare the various approaches in terms of prediction quality and computational cost.
Article analysis:
The article is written in an objective manner and provides a comprehensive overview of the literature on network traffic prediction. The authors provide a detailed explanation of the mathematical formulation of each technique and explain their internal operation with intuitive figures. The authors also present numerical experiments comparing all the presented approaches in presence of real-world mobile traffic demands when considering different configurations for the statistical and neural networks approaches.
The article is reliable and trustworthy as it provides an unbiased overview of existing techniques for network traffic forecasting. The authors have provided sufficient evidence to support their claims by providing numerical experiments with real-world data sets which allows readers to compare the various approaches directly in terms of fitting quality and computational costs. Furthermore, they have made their code publicly available so that readers can readily access a wide range of forecasting tools and use them as benchmarks for more advanced solutions.
The article does not appear to be biased or one-sided in its reporting or presentation of information; it provides an objective overview of existing techniques for network traffic forecasting without promoting any particular approach over another. Additionally, all relevant points are considered and explored thoroughly throughout the article without leaving out any important information or counterarguments that could affect its reliability or trustworthiness.