Internet Traffic Forecasting using Neural Networks
Internet Traffic Forecasting using Neural Networks
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DOI：
10.1109/ijcnn.2006.247142
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
P. Cortez;M. Rio;Miguel Rocha;Pedro Sousa
中科院分区：
文献类型：
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作者：
P. Cortez;M. Rio;Miguel Rocha;Pedro Sousa
网络流量预测是计算机网络领域中一个很少受到重视的重要问题。通过改进此任务，可以创建高效的流量工程和异常检测工具，从而从更好的资源管理中获得经济收益。本文从时间序列预测（TSF）的角度提出了一种用于TCP/IP流量预测的神经网络集成（NNE）。通过考虑来自两家大型互联网服务提供商的真实数据，设计了几个实验。此外，还分析了不同的时间尺度（如每五分钟和每小时）和预测范围。总的来说，与其他TSF方法（如Holt-Winters和ARIMA）相比，NNE方法具有竞争力。
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).