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Statistical Error Analysis on Recording LRD Traffic Time Series

机译:记录LRD交通时间序列的统计误差分析

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摘要

Measurement of LRD traffic time series is the first stage to experimental research of traffic patterns. From a view of measurement, if the length of a measured series is too short, an estimate of a specific objective (e.g., autocorrelation function) may not achieve a given accuracy. On the other hand, if a measured series is over-long, it will be too much for storage space and cost too much computation time. Thus, a meaningful issue in measurement is how to determine the record length of an LRD traffic series with a given degree of accuracy of the estimate of interest. In this paper, we present a formula for requiring the record length of LRD traffic series according to a given bound of accuracy of autocorrelation function estimation of fractional Gaussian noise and a given value of H. Further, we apply our approach to assessing some widely used traces in the traffic research, giving a theoretical evaluation of those traces from a view of statistical error analysis.
机译:LRD交通时间序列的测量是交通模式实验研究的第一步。从测量的角度看,如果测量的序列的长度太短,则特定目标的估计(例如,自相关函数)可能无法达到给定的精度。另一方面,如果测得的序列过长,则对于存储空间而言将太多,并且会花费过多的计算时间。因此,测量中的一个有意义的问题是如何在给定的兴趣估计准确度的情况下确定LRD业务序列的记录长度。在本文中,我们根据分数高斯噪声的自相关函数估计的精度和给定的H值的给定范围,提出了要求LRD业务序列的记录长度的公式。此外,我们将我们的方法用于评估一些广泛使用的方法。交通研究中的痕迹,从统计误差分析的角度对这些痕迹进行了理论评估。

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