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

作者：Li Ming^*; Zhang Peidong; Leng Jianxing

来源：Physica A: Statistical Mechanics and its Applications , 2016, 445: 189-199.

DOI：10.1016/j.physa.2015.10.071

• Hurst parameter estimation
• Autocorrelation regression
• Golden section search
• Fractional Gaussian noise with long-range dependence

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.

• 单位
  浙江大学; 华东师范大学