This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behaviour, standard deviation, and Mean Squared Error using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed computer network.

翻译：文章探索了高速计算机网络所需的时间序列点数,以准确估计赫斯特指数。 方法包括设计一个实验, 使用用于获取高速网络流量所得出时间序列地址的测算器, 并随后处理获得赫斯特指数准确估计所需的最低点数。 方法涉及赫斯特指数的详尽分析, 考虑偏差行为、 标准偏差 和平均平方错误 。 我们的结果表明, 惠特尔测算器成功估算了赫斯特指数的分数, 分数数是几分数。 根据所获得的结果, 以实验方式提出了时间序列最小长度。 最后, 为了验证结果, 该方法应用于高速计算机网络的实际交通捕捉。