Research on Combined Prediction Model for Busy Telephone Traffic

Autor:	Xi Zhong Qin, Jiang Bao Li, Lei Sheng, Li Chen, Zhen Hong Jia
Rok vydání:	2014
Předmět:	Engineering business.industry Stability (learning theory) Particle swarm optimization General Medicine computer.software_genre Traffic prediction Support vector machine Transformation (function) Wavelet Component (UML) Data mining business computer
Zdroj:	Applied Mechanics and Materials. 610:789-796
ISSN:	1662-7482
DOI:	10.4028/www.scientific.net/amm.610.789
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::25273010f9f4fec99b740ebfc38555fd https://doi.org/10.4028/www.scientific.net/amm.610.789 Zobrazit plný text záznamu