Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jfranklin.2003.09.002
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dc.titleA correlation-based computational model for synthesizing long-range dependent data
dc.contributor.authorLi, M.
dc.contributor.authorChi, C.-H.
dc.date.accessioned2013-07-04T07:44:55Z
dc.date.available2013-07-04T07:44:55Z
dc.date.issued2004
dc.identifier.citationLi, M., Chi, C.-H. (2004). A correlation-based computational model for synthesizing long-range dependent data. Journal of the Franklin Institute 340 (6-7) : 503-514. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jfranklin.2003.09.002
dc.identifier.issn00160032
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/39584
dc.description.abstractThe authors present a computation model to generate long-range dependent (LRD) data according to a given correlation structure by filtering white noise. The simulation procedure of LRD data according to a given correlation structure is explained. A case study is illustrated with real LRD data series in the Internet. © 2003 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.jfranklin.2003.09.002
dc.sourceScopus
dc.subjectBrownian motion
dc.subjectFilters
dc.subjectFourier analysis
dc.subjectFractional Gaussian noise
dc.subjectLong-range dependent data generation
dc.subjectTeletraffic
dc.typeArticle
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1016/j.jfranklin.2003.09.002
dc.description.sourcetitleJournal of the Franklin Institute
dc.description.volume340
dc.description.issue6-7
dc.description.page503-514
dc.description.codenJFINA
dc.identifier.isiut000188952500009
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