A local least square support vector machine prediction algorithm of small scale network traffic based on correlation analysis
Authors:
- Tang Zhou-JinTZTang Zhou-Jin
- Peng TaoPTPeng Tao
- Wang Wen-BoWWWang Wen-Bo
- 1 January 2014
- journal article
- Published by Acta Physica Sinica, Chinese Physical Society and Institute of Physics, Chinese Academy of Sciences in Acta Physica Sinica
- Vol. 63 (13) , 130504
Abstract
Real-time monitoring and forecasting technology for network traffic has played an important role in network management. Effective network traffic prediction could analyze and solve problems before overload occurs, which significantly improves network availability. In this paper, after the vulnerability of traditional nonlinear prediction method in forecasting modeling is analyzed, the relevant local (RL) forecast which is based on correlation analysis and the parameter optimization method based on pattern search (PS) is introduced. Using the correlation analysis, the optimal training subset is chosen from time-and distance-correlated training samples. On this basis, the prediction model is established by LSSVM. Finally network traffic dataset collected from wired campus networks is studied for our experiments. And the results show that the relevant local LSSVM prediction method whose training set and parameters have been automatically optimized can effectively predict the small scale traffic measurement data, and RL-LSSVM traffic forecasting algorithm exhibits significantly good prediction accuracy for the data set compared with previous algorithm.Keywords
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