Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series

EasyChair Preprint no. 5141

Abstract

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.

Keyphrases: fractality, High-speed computer network, Hurst exponent, time series, traffic flows

@Booklet{EasyChair:5141,
  author = {Ginno Millán and Román Osorio-Comparán and Gastón Lefranc},
  title = {Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series},
  howpublished = {EasyChair Preprint no. 5141},

  year = {EasyChair, 2021}}