Long-range dependence analysis of Internet traffic

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Long-range-dependent time series are endemic in the statistical analysis of Internet traffic. The Hurst parameter provides a good summary of important self-similar scaling properties. We compare a number of different Hurst parameter estimation methods and some important variations. This is done in the context of a wide range of simulated, laboratory-generated, and real data sets. Important differences between the methods are highlighted. Deep insights are revealed on how well the laboratory data mimic the real data. Non-stationarities, which are local in time, are seen to be central issues and lead to both conceptual and practical recommendations.
Publisher
TAYLOR & FRANCIS LTD
Issue Date
2011
Language
English
Article Type
Article
Citation

JOURNAL OF APPLIED STATISTICS, v.38, no.7, pp.1407 - 1433

ISSN
0266-4763
DOI
10.1080/02664763.2010.505949
URI
http://hdl.handle.net/10203/285760
Appears in Collection
MA-Journal Papers(저널논문)
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