  This article explores the required amount of time series points from a
high-speed traffic network to accurately estimate the Hurst exponent. The
methodology consists in designing an experiment using estimators that are
applied to time series, followed by addressing the minimum amount of points
required to obtain accurate estimates of the Hurst exponent in real-time. The
methodology addresses the exhaustive analysis of the Hurst exponent considering
bias behavior, standard deviation, mean square error, and convergence 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 network based
on the IEEE 802.3ab standard.
