QQ客服 官方微博 反馈留言

	Network traffic prediction based on a new time series model
	Yin, H ; Lin, C ; Sebastien, B ; Li, B ; Min, GY
	2010-05-06 ; 2010-05-06 ; OCT
关键词	time series model network traffic online prediction memory-shortening techniques INTERNET Engineering, Electrical & Electronic Telecommunications
中文摘要	Fast and accurate methods for predicting traffic properties and trend are essential for dynamic network resource management and congestion control. With the aim of performing online and feasible prediction of network traffic, this paper proposes a novel time series model, named adaptive autoregressive (AAR). This model is built upon an adaptive memory-shortening technique and an adaptive-order selection method originally developed by this study. Compared to the conventional one-step ahead prediction using traditional Box-Jenkins time series models (e.g. AR, MA, ARMA, ARIMA and ARFIMA), performance results obtained from actual Internet traffic traces have demonstrated that the proposed AAR model is able to support online prediction of dynamic network traffic with reasonable accuracy and relatively low computation complexity. Copyright (c) 2005 John Wiley & Sons, Ltd.
语种	英语 ; 英语
出版者	JOHN WILEY & SONS LTD ; CHICHESTER ; THE ATRIUM, SOUTHERN GATE, CHICHESTER PO19 8SQ, W SUSSEX, ENGLAND
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/10259]
专题	清华大学
推荐引用方式 GB/T 7714	Yin, H,Lin, C,Sebastien, B,et al. Network traffic prediction based on a new time series model[J],2010, 2010, OCT.
APA	Yin, H,Lin, C,Sebastien, B,Li, B,&Min, GY.(2010).Network traffic prediction based on a new time series model..
MLA	Yin, H,et al."Network traffic prediction based on a new time series model".(2010).

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。