Network traffic prediction based on LSSVM optimized by PSO

doi:10.1109/UIC-ATC-ScalCom.2014.100

	Network traffic prediction based on LSSVM optimized by PSO
	Yang, Yi 2; Chen, Yanhua 2; Li, Caihong 2; Gui, Xiangquan 1; Li, Lian 2
	2014
关键词	Network traffic prediction Particle swarm optimization Least square support vector machine
DOI	10.1109/UIC-ATC-ScalCom.2014.100
页码	829-834
英文摘要	Nowadays, artificial intelligence is frequently used to various fields including medicine, chemistry and forecasting. In this paper, artificial intelligence is applied to network traffic prediction. Due to that network traffic prediction plays an important role in network management, planning, traffic congestion control and traffic engineering. Seeking for more accurate network traffic prediction techniques, this paper proposed a new hybrid method (SPLSSVM) which based on seasonal adjustment (SA) and least squares support vector machine (LSSVM) optimized by particle swarm optimization (PSO) to predict network traffic. The proposed method is examined by using the network traffic data from Lanzhou University. Empirical testing indicates that the proposed method can provide more accurate and effective results than the other forecasting methods.
会议录	2014 IEEE 11TH INTL CONF ON UBIQUITOUS INTELLIGENCE AND COMPUTING AND 2014 IEEE 11TH INTL CONF ON AUTONOMIC AND TRUSTED COMPUTING AND 2014 IEEE 14TH INTL CONF ON SCALABLE COMPUTING AND COMMUNICATIONS AND ITS ASSOCIATED WORKSHOPS
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
资助项目	Natural Science Foundation of P. R. of China[61073193][61300230] ; Key Science and Technology Foundation of Gansu Province[1102FKDA010] ; Natural Science Foundation of Gansu Province[1107RJZA188] ; Science and Technology Support Program of Gansu Province[1104GKCA037] ; National Science and Technology Support Program[2012BAF12B19]
WOS研究方向	Computer Science
WOS记录号	WOS:000410667100122
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36732]
专题	计算机与通信学院
通讯作者	Chen, Yanhua
作者单位	1.Lanzhou Univ Technol, Coll Comp & Commun, Lanzhou 730000, Gansu, Peoples R China 2.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Yang, Yi,Chen, Yanhua,Li, Caihong,et al. Network traffic prediction based on LSSVM optimized by PSO[C]. 见:.