Long-range dependence analysis of Internet traffic |
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Authors: | Cheolwoo Park Félix Hernández-Campos Long Le J S Marron Juhyun Park Vladas Pipiras |
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Institution: | 1. Department of Statistics , University of Georgia , Athens, GA, 30602-1952, USA;2. Google Inc. , Mountain View, CA, 94043, USA;3. Department of Computer Science , University of North Carolina , Chapel Hill, NC, 25799-3175, USA;4. Department of Statistics and Operations Research , University of North Carolina , Chapel Hill, NC, 25799-3260, USA;5. Department of Mathematics and Statistics , Lancaster University , Lancaster, LA1 4YF, UK |
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Abstract: | 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. |
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Keywords: | Hurst parameter Internet traffic long-range dependence multiscale analysis non-stationarity |
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