时域操作下R/S统计实现的行为

J. Pacheco
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引用次数: 2

摘要

当前通信网络流量的复杂相关结构要求使用算法来检测网络流量过程样本路径中的依赖程度。实现上述目标的几种算法具有不同程度的精度和收敛性。本文研究了R/S统计量,特别是在时间聚合条件下对几种长记忆特征(fGn和FARIMA)的准确性、收敛性和有效性。我们提出了三种R/S统计量的线性拟合中低端和高端的最佳值的选择。在此基础上,我们证明了在时间聚合下,不同的实现具有不同程度的准确性、收敛性和有效性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Behavior of R/S Statistic Implementations under Time-Domain Operations
Complex correlation structure of current communications network traffic requires the use of algorithms for detecting the degree of dependence in a sample path of the network traffic process. Several algorithms for accomplishing the above exists with varying degrees of accuracy and convergence. In this paper we study the R/S statistic, in particular, the accuracy, convergence and its effectiveness under time aggregation for several long-memory characteristics (fGn and FARIMA). We present the selection of the optimal values of the low and high-ends in the linear fit for three implementations of the R/S statistic. Based on the above we show that different implementations give varying degrees of accuracy, convergence and effectiveness under time aggregation
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