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Internet Traffic Forecasting using Neural Networks

Paulo Cesar Cortez · Miguel Rio C · Miguel Rocha · Pedro Sousa

119 citations0/0 files storedInternational Joint Conference on Neural Networks (IJCNN)

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Forecasting Techniques and Applications Network Traffic and Congestion Control Traffic Prediction and Management Techniques

Abstract

The forecast of Internet traffic is an important issue that has received few attention from the computer networks field. By improving this task, efficient traffic engineering and anomaly detection tools can be created, resulting in economic gains from better resource management. This paper presents a neural network ensemble (NNE) for the prediction of TCP/IP traffic using a time series forecasting (TSF) point of view. Several experiments were devised by considering real-world data from two large Internet Service Providers. In addition, different time scales (e.g. every five minutes and hourly) and forecasting horizons were analyzed. Overall, the NNE approach is competitive when compared with other TSF methods (e.g. Holt-Winters and ARIMA).

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1977Time Series Analysis: Forecasting and Control 1993Forecasting methods and applications 1960Forecasting Sales by Exponentially Weighted Moving Averages

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