Please use this identifier to cite or link to this item: http://repository.unmul.ac.id/handle/123456789/3264
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHaviluddin, Haviluddin-
dc.contributor.authorAlfred, Rayner-
dc.date.accessioned2019-12-13T18:30:08Z-
dc.date.available2019-12-13T18:30:08Z-
dc.date.issued2014-09-03-
dc.identifier.issn1793-8244-
dc.identifier.urihttp://repository.unmul.ac.id/handle/123456789/3264-
dc.description.abstractThis 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.en_US
dc.language.isoenen_US
dc.publisherJournal of Advances in Computer Networks (JACN)en_US
dc.subjectNetwork traffic, ARIMA, time series, forecastingen_US
dc.titleForecasting Network Activities Using ARIMA Methoden_US
dc.typeArticleen_US
Appears in Collections:J - Computer Sciences and Information Technology

Files in This Item:
File Description SizeFormat 
18. 106-CS024.pdf14 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.