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). |
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