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01-Applied Mathematics & Information Sciences
An International Journal


Volumes > Volume 9 > No. 2L


A Small Scale Forecasting Algorithm for Network Traffic based on Relevant Local Least Squares Support Vector Machine Regression Model

PP: 653-659
Tao Peng, Zhoujin Tang,
Real-time monitoring and forecasting technology for network traffic continues to play an important role in network management. Effective network traffic prediction detects and avoids potential overload problems before they occur, which significantly improves network availability and stability. Recent research has centered around Time Series Analysis based traffic prediction methods that primarily extend Neural Network Forecasting (NNF) and Least Squares Support Vector Machine (LSSVM) algorithms, which are not without their drawbacks. Given the vulnerabilities of existing nonlinear prediction methods in forecasting modeling, this paper presents a novel, Relevant Local (RL) forecast method and its accompanying Pattern Search (PS) parameter-optimization approach to introduce a new small-scale network traffic forecasting algorithm called RL-LSSVM (Relevant Local-Least squares support vector machine). Furthermore, we demonstrate our new algorithm on network traffic data collected from wired campus networks and show that the RL-LSSVM can effectively predict the small scale traffic measurement data while exhibit significantly improved prediction accuracy than existing algorithms.

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