Long-range dependence analysis of Internet traffic

Autor:	James Stephen Marron, Michele Trovero, F. D. Smith, Zhengyuan Zhu, Long Le, Cheolwoo Park, Vladas Pipiras, Richard Smith, Félix Hernández-Campos, Juhyun Park
Rok vydání:	2010
Předmět:	Statistics and Probability Hurst exponent Series (mathematics) Computer science Context (language use) Internet traffic Dependence analysis computer.software_genre Range (mathematics) Statistical analysis Data mining Statistics Probability and Uncertainty Scaling computer
Zdroj:	Journal of Applied Statistics. 38:1407-1433
ISSN:	1360-0532 0266-4763
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c8ddf3e15eeb7005122c21ca0a2efb4b https://doi.org/10.1080/02664763.2010.505949 Zobrazit plný text záznamu