Long-range dependence analysis of Internet traffic
Long-range dependence analysis of Internet traffic
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DOI：
10.1080/02664763.2010.505949
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发表时间：
2011-07
影响因子：
1.5
通讯作者：
Cheolwoo Park;Félix Hernández-Campos;L. Le;J. Marron;Juhyun Park;V. Pipiras;F. D. Smith;R. Smith;M. Trovero;Zhengyuan Zhu-
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文献类型：
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作者：
Cheolwoo Park;Félix Hernández-Campos;L. Le;J. Marron;Juhyun Park;V. Pipiras;F. D. Smith;R. Smith;M. Trovero;Zhengyuan Zhu-
长期依赖的时间序列是互联网流量统计分析中的一个常见问题。Hurst参数提供了重要的自相似标度性质的很好的总结。我们比较了一些不同的赫斯特参数估计方法和一些重要的变化。这是在广泛的模拟、实验室生成和真实的数据集的背景下完成的。突出了方法之间的重要差异。揭示了实验室数据如何模拟真实的数据的深刻见解。非平稳性，这是当地的时间，被视为中心问题，并导致概念和实际的建议。
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.