Improving autocorrelation regression for the Hurst parameter estimation of long-range dependent time series based on golden section search

摘要

This article presents an improved autocorrelation correlation ACF) regression method of estimating the Hurst parameter of a time series with long-range dependence (LRD) by using golden section search (GSS). We shall show that the present method is substantially efficient than the conventional ACF regression method of H estimation. Our research uses fractional Gaussian noise as a data case but the method introduced is applicable to time series with LRD in general.

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