The most important characteristic of a covariance stationary self-similar stochastic process is that it is long-range dependent. The long-range dependent time series hold significant correlations across arbitrarily large time scales. And the Hurst parameter H measure the degree of long-range dependence and can be estimated by several methods.
Excellent and stable implementations for Hurst.
I am curious about the rationale for the cut-off points ( n/10 and 6n/10 for Higuchi and AbsVal and 3n/10 and 9n/10 for R/S) as this is the only thing that seems a bit arbitrary in the code, but otherwise excellent algorithm.
What methods have you used to generate the input sequence?I have used your codes, and in higuchi methode I found hurst parameter greater than 1 and in more methods I found hurst parameter least than 0.5.please tell me why?thanks
I had some H greater than 1 when i run this code. could you tell why? thx
Great peace of code! I was looking long time around to find it.
- Maybe the various methods could briefly be summarized so that terminological variations can be cleared.
I would like to estimate the Hurst coefficient so I can determine if my data series is persistent (H>0.5), antipersistent (H<0.5), or neutral (H=0.5). Several of the estimates I get are, for example, H = 1.8, or H=1.6, H=0.9....should I disregard the whole number and concentrate only on the decimals for interpreting persistence?
great¡, it works very well.
while running the program graph is not coming it is showing error that undefined isplot so kindly suggest me how can i over come this error
I have used your RS routine, and in some cases I found hurst parameter greater than 1 (one), is it anything wrong? Thanks.
Thanks a lot! Very good!
Good job, usefull package to esitmate Hurst coef, thanks
very good, and contain different methods to estimate hurst parameter
A very good package combining different methods. Recommended for studing purposes and developpment of own algorithms
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