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Long-range dependence analysis of Internet traffic

Research output: Contribution to journalJournal article


  • Cheolwoo Park
  • Felix Hernandez-Campos
  • Long Le
  • J. S. Marron
  • Juhyun Park
  • Vladas Pipiras
  • F. D. Smith
  • Richard L. Smith
  • Michele Trovero
  • Zhengyuan Zhu
Journal publication date2011
JournalJournal of Applied Statistics
Number of pages27
Original languageEnglish


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.