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Robust prediction of network traffic using Quantile Regression Models

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3 Author(s)
Wei Biao Wu ; Department of Statistics, The University of Chicago, Chicago, IL 60637 USA. ; Zhiwei Xu ; Yu Wang

Reliable network traffic prediction is essential for efficient resource management schemes. Based on the quantile regression, we propose a robust prediction procedure which is resistent to outliers. For long-term predictions, the predicting intervals have a coverage probability that is very close to the pre-assigned nominal level. The detailed distributional information of the estimated quantities can be efficiently characterized by using different quantiles. The performance of the prediction is tested on a large telecommunication network traffic data. The results indicate that the proposed quantile regression provide relative accurate prediction and is not sensitive to outliers

Published in:

2006 IEEE International Conference on Information Reuse & Integration

Date of Conference:

16-18 Sept. 2006