Multi‐scale Internet traffic forecasting using neural networks and time series methods

By Paulo Cortez, Miguel Rio, Miguel Rocha, Pedro Sousa. 2010

203 citations recorded by the source corpus.

This article presents three methods to forecast accurately the amount of traffic in TCP/IP based networks: a novel neural network ensemble approach and two important adapted time series methods (ARIMA and Holt‐Winters). In order to assess their accuracy, several experiments were held using real‐world data from two large Internet service providers. In addition, different time scales (5 min, 1 h and 1 day) and distinct forecasting lookaheads were analysed. The experiments with the neural ensemble achieved the best results for 5 min and hourly data, while the Holt‐Winters is the best option for the daily forecasts. This research opens possibilities for the development of more efficient traffic engineering and anomaly detection tools, which will result in financial gains from better network resource management.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

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Citations in the corpus

Correcting Forecasts with Multifactor Neural Attention (2016, background): “, 2011), (Cortez et al., 2012), (Marvuglia & Messineo, 2012), (Neupane et al.”
Deep Learning for Time-Series Analysis (2017, methodology): “More recent approaches include usage of Elman RNNs to predict chaotic TimeSeries [10], employing ANN ensemble methods for forecasting Internet traffic [12], using simple Multilayer Perceptrons for modeling the amount of littering in the North Sea [53], and implementing in FPGA a prediction algorithm”