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 > > ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.