Wireless Traffic Modeling and Prediction Using Seasonal ARIMA Models(Network)

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概要

• 論文の詳細を見る

• Seasonal ARIMA model is a good traffic model capable of capturing the behavior of a network traffic stream. In this paper, we give a general expression of seasonal ARIMA models with two periodicities and provide procedures to model and to predict traffic using seasonal ARIMA models. The experiments conducted in our feasibility study showed that seasonal ARIMA models can be used to model and predict actual wireless traffic such as GSM traffic in China.

• 社団法人電子情報通信学会の論文
• 2005-10-01

著者

• YANG Oliver

  CCNR Lab, School of Information Technology and Engineering, University of Ottawa

• YANG Oliver

  Faculty CCNR Lab, School of Information Technology and Engineering University of Ottawa

• Shu Yantai

  Tianjin Univ. Tianjin Chn

• Shu Yantai

  Department Of Computer Science Tianjin University

• Yang Oliver

  Ccnr Lab School Of Information Technology And Engineering University Of Ottawa

• YU Minfang

  China Academy of Space Technology

• LIU Jiakun

  School of Science, Tianjin University

• FENG Huifang

  Department of Computer Science, Tianjin University

• Feng Huifang

  Northwest Normal Univ. Lanzhou Chn

• Liu Jiakun

  School Of Science Tianjin University

関連論文

• WLAN Traffic Prediction Using Support Vector Machine
• Performance Improvement for 802.11 Based Wireless Local Area Networks(Wireless Communication Technologies)
• Study of Medium Access Delay in IEEE802.11 Wireless Networks(Wireless Communication Technologies)
• Wireless Traffic Modeling and Prediction Using Seasonal ARIMA Models(Network)
• Flow-Level Multipath Load Balancing in MPLS Network(Network)

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