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Short period prediction is a relevant task for many network applications. Tuning the parameters of the prediction model is very crucial to achieve accurate prediction. This paper focuses on the design, the empirical evaluation and the analysis of the behavior of training-based models for predicting the throughput of a single link i.e. the incoming input rate in Megabit per second. In this work, a neurofuzzy model, the AutoRegressive Moving Average (ARMA) model and the Integrated AutoRegressive Moving Average (ARIMA) model are used for predicting. Via experimentation on real network traffic of different links, the authors study the effect of some parameters on the prediction performance in terms of error.
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