Lorenzo Isella <lorenzo.isella <at> gmail.com>
writes:>
> Dear All,
> I am a novice when it comes to time-series analysis and at the moment I
> am actually interested in calculating the Hurst exponent of a time
> series.
Some time ago I tested some of the classical chaotic time series
(such as the logistic map and others, no financial time series) with
available functions in R and Matlab. In my experience, Peng's method
(realized in R as fArma::pengFit) works reasonably reliable and is
more accurate than most others on these series.
Unfortunately, the available R and Matlab implementations of the same
method -- and refering back to the same literature article -- can give
quite different results, with varying success for both sides.
AFAIK, in TISEAN there is no function estimating the Hurst exponent.
Regards
Hans Werner
> This question has already been asked quite some time ago
>
> http://bit.ly/98dZsi
>
> and I trust some progress has been made ever since.
> I was able to find some functions in the packages
>
> http://cran.r-project.org/web/packages/Rwave/index.html and
> http://cran.r-project.org/web/packages/fArma/index.html
>
> Allegedly, there should be functions for this in the Rtisean package
>
> http://cran.r-project.org/web/packages/RTisean/index.html
>
> but I have not been able to find them.
> Bottom line: if you have a time series (list of empirical data of
> varying length and not necessarily sampled on a uniform time grid) what
> R tool would you use to estimate its Hurst exponent?
> Any suggestion is appreciated.
> Cheers
>
> Lorenzo
>
>