  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.
