Journal or Book Title
Journal of Applied Statistics
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.
Taylor & Francis
Park, Cheolwoo; Google, Inc.; Le, Long; Marron, J. S.; Park, Juhyun; Pipiras, Vladas; Smith, F. D.; Smith, Richard L.; Trovero, Michele; and Zhu, Zhengyuan, "Long-range dependence analysis of Internet traffic" (2011). Statistics Publications. 137.