Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Long-range dependence analysis of Internet traffic
AU - Park, Cheolwoo
AU - Hernandez-Campos, Felix
AU - Le, Long
AU - Marron, J. S.
AU - Park, Juhyun
AU - Pipiras, Vladas
AU - Smith, F. D.
AU - Smith, Richard L.
AU - Trovero, Michele
AU - Zhu, Zhengyuan
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Hurst parameter
KW - Internet traffic
KW - long-range dependence
KW - multiscale analysis
KW - non-stationarity
U2 - 10.1080/02664763.2010.505949
DO - 10.1080/02664763.2010.505949
M3 - Journal article
VL - 38
SP - 1407
EP - 1433
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
SN - 0266-4763
IS - 7
ER -