hurst parameter estimation

In our paper, the aforementioned items will become represented … Data traffic traces are known to be bursty with long range dependence. Chu Chen (view profile) 1 file; 7 downloads; 4.9. ABSTRACT. This routine estimate the long-range dependence of a sequence with several methods. We will estimate the Hurst parameter together with the model coefficients that will be linearly involved in the representation of the entire SDE—to be more precise, in the time-discrete approximation which we shall study. We estimate the Hurst parameter H of a fractional Brow-nian motion from discrete noisy data, observed along a high frequency sampling scheme. 7 Downloads. View License × License. This estimation will be based on given data and supported by modern optimization techniques. Another way to state this is that a random process with an underlying trend has some degree of autocorrelation (more on this below). 11 Ratings. In this paper we present a rigorous study of the MAVAR log-regression estimator. Updated 11 Mar 2008. Hurst parameter estimation from noisy observations of data traffic traces. In order to estimate the memory parameter of Internet traffic data, it has been recently proposed a log-regression estimator based on the so-called modified Allan variance (MAVAR). The naive researcher taking a data set and running it through an off-the-shelf method for estimating the Hurst parameter is likely to end up with a misleading answer or possibly several different misleading answers. 4.9. version 1.0.0.0 (93.1 KB) by Chu Chen. Estimates of the Hurst parameter for the Web traffic in the online stores range from 0.6 for low traffic to 0.85 for heavy traffic. Previous Chapter Next Chapter. HURST PARAMETER ESTIMATION FOR EPILEPTIC SEIZURE DETECTION 171 which R/S, DA and bdSWV are sensitive. Estimating the Hurst exponent for a data set provides a measure of whether the data is a pure white noise random process or has underlying trends. The presence of the systematic experimental noise makes harder the recovering of H since relevant information is mostly contained in the high frequencies of the signal. Despite their mathematical similarity, the estimates they yield when applied to the same signal, are quantitatively and qualitatively different (Fig 3). Simulations have shown that this estimator achieves higher accuracy and better confidence when compared with other methods. Pages 193–198. Follow; Download. View. hurst parameter estimate. The exact self-similarity model of long range dependence can pose analytical and practical problems at very small and very large time lags. Various techniques are tried to filter real-life traces in addition to making measurements purely on the raw data.

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