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ISNN
2005
Springer

Internet Traffic Prediction by W-Boost: Classification and Regression

13 years 9 months ago
Internet Traffic Prediction by W-Boost: Classification and Regression
Abstract. Internet traffic prediction plays a fundamental role in network design, management, control, and optimization. The self-similar and non-linear nature of network traffic makes highly accurate prediction difficult. In this paper, we proposed a new boosting scheme, namely W-Boost, for traffic prediction from two perspectives: classification and regression. To capture the nonlinearity of the traffic while introducing low complexity into the algorithm, ‘stump’ and piece-wise-constant function are adopted as weak learners for classification and regression, respectively. Furthermore, a new weight update scheme is proposed to take the advantage of the correlation information within the traffic for both models. Experimental results on real network traffic which exhibits both self-similarity and non-linearity demonstrate the effectiveness of the proposed W-Boost.
Hanghang Tong, Chongrong Li, Jingrui He, Yang Chen
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ISNN
Authors Hanghang Tong, Chongrong Li, Jingrui He, Yang Chen
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