使用ARIMA方法预测网络活动

H. Haviluddin, R. Alfred
{"title":"使用ARIMA方法预测网络活动","authors":"H. Haviluddin, R. Alfred","doi":"10.7763/JACN.2014.V2.106","DOIUrl":null,"url":null,"abstract":"This paper presents an approach for a network traffic characterization by using an ARIMA (Autoregressive Integrated Moving Average) technique. The dataset used in this study is obtained from the internet network traffic activities of the Mulawarman University for a period of a week. The results are obtained using the Box-Jenkins Methodology. The Box-Jenkins methodology consists of five ARIMA models which include ARIMA (2, 1, 1) (1, 1, 1) ¹², ARIMA (1, 1, 1) (1, 1, 1) ¹², ARIMA (2, 1, 0) (1, 1, 1) ¹², ARIMA (0, 1, 0) (1, 1, 1) ¹², and ARIMA (0, 1, 0) (1, 2, 1) ¹². In this paper, ARIMA (0, 1, 0) (1, 2, 1) ¹² was selected as the best model that can be used to model the internet network traffic.","PeriodicalId":232851,"journal":{"name":"Journal of Advances in Computer Networks","volume":"184 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Forecasting network activities using ARIMA method\",\"authors\":\"H. Haviluddin, R. Alfred\",\"doi\":\"10.7763/JACN.2014.V2.106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents an approach for a network traffic characterization by using an ARIMA (Autoregressive Integrated Moving Average) technique. The dataset used in this study is obtained from the internet network traffic activities of the Mulawarman University for a period of a week. The results are obtained using the Box-Jenkins Methodology. The Box-Jenkins methodology consists of five ARIMA models which include ARIMA (2, 1, 1) (1, 1, 1) ¹², ARIMA (1, 1, 1) (1, 1, 1) ¹², ARIMA (2, 1, 0) (1, 1, 1) ¹², ARIMA (0, 1, 0) (1, 1, 1) ¹², and ARIMA (0, 1, 0) (1, 2, 1) ¹². In this paper, ARIMA (0, 1, 0) (1, 2, 1) ¹² was selected as the best model that can be used to model the internet network traffic.\",\"PeriodicalId\":232851,\"journal\":{\"name\":\"Journal of Advances in Computer Networks\",\"volume\":\"184 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advances in Computer Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7763/JACN.2014.V2.106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advances in Computer Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7763/JACN.2014.V2.106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27

摘要

本文提出了一种利用ARIMA(自回归综合移动平均)技术表征网络流量的方法。本研究使用的数据集来自Mulawarman大学为期一周的互联网网络流量活动。使用Box-Jenkins方法获得结果。Box-Jenkins方法包括五个ARIMA模型包括ARIMA(2, 1, 1)(1, 1, 1)¹²,ARIMA(1, 1, 1)(1, 1, 1)¹²,ARIMA(2 1 0)(1, 1, 1)¹²,ARIMA(0,1,0)(1, 1, 1)¹²,,ARIMA(0,1,0)(1、2、1)¹²。本文选择ARIMA (0,1,0) (1,2,1) ¹²作为可用于互联网网络流量建模的最佳模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Forecasting network activities using ARIMA method
This paper presents an approach for a network traffic characterization by using an ARIMA (Autoregressive Integrated Moving Average) technique. The dataset used in this study is obtained from the internet network traffic activities of the Mulawarman University for a period of a week. The results are obtained using the Box-Jenkins Methodology. The Box-Jenkins methodology consists of five ARIMA models which include ARIMA (2, 1, 1) (1, 1, 1) ¹², ARIMA (1, 1, 1) (1, 1, 1) ¹², ARIMA (2, 1, 0) (1, 1, 1) ¹², ARIMA (0, 1, 0) (1, 1, 1) ¹², and ARIMA (0, 1, 0) (1, 2, 1) ¹². In this paper, ARIMA (0, 1, 0) (1, 2, 1) ¹² was selected as the best model that can be used to model the internet network traffic.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
An Improved Group Key Agreement Scheme with Privacy Preserving Based on Chaotic Maps Clustering and Feature Selection Technique for Improving Internet Traffic Classification Using K-NN Does the IEEE 802.15.4 MAC Protocol Work Well in Wireless Body Area Networks TXOP Combinatorial Problem in IEEE 802.11e HCCA Networks Security Threats of URL Shortening: A User's Perspective
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1