Research on Combined Prediction Model for Busy Telephone Traffic

Abstract:

In order to improve the prediction accuracy of busy telephone traffic, this study proposes a busy telephone traffic prediction method that combines wavelet transformation and least square support vector machine (lssvm) model which is optimized by particle swarm optimization (pso) algorithm. Firstly, decompose the pretreatment of busy telephone traffic data with mallat algorithm and get low frequency component and high frequency component. Secondly, reconfigure each component and use pso_lssvm model predict each reconfigured one. Then the busy telephone traffic can be achieved. The experimental results show that the prediction model has higher prediction accuracy and stability.

• Periodical: Applied Mechanics and Materials (Volume 610)
• Pages: 789-796
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.610.789
• Online since: August 2014
• Authors: Jiang Bao Li*, Zhen Hong Jia, Xi Zhong Qin, Lei Sheng, Li Chen
• Keywords: Least Square Support Vector Machine, Particle Swarm Optimization Algorithm, Prediction, Wavelet Transformation

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