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.