https://github.com/cran/pracma
Tip revision: 00dcc24912cb9162914b67ae18137cab456a0b21 authored by Hans W. Borchers on 06 September 2016, 16:40:55 UTC
version 1.9.5
version 1.9.5
Tip revision: 00dcc24
hurst.Rd
\name{hurstexp}
\alias{hurstexp}
\title{
Hurst Exponent
}
\description{
Calculates the Hurst exponent using R/S analysis.
}
\usage{
hurstexp(x, d = 50, display = TRUE)
}
\arguments{
\item{x}{a time series.}
\item{d}{smallest box size; default 50.}
\item{display}{logical; shall the results be printed to the console?}
}
\details{
\code{hurstexp(x)} calculates the Hurst exponent of a time series \code{x}
using R/S analysis, after Hurst, with slightly different approaches, or
corrects it with small sample bias, see for example Weron.
These approaches are a corrected R/S method, an empirical and corrected
empirical method, and a try at a theoretical Hurst exponent. It should be
mentioned that the results are sometimes very different, so providing error
estimates will be highly questionable.
Optimal sample sizes are automatically computed with a length that
possesses the most divisors among series shorter than \code{x} by no more
than 1 percent.
}
\value{
\code{hurstexp(x)} returns a list with the following components:
\itemize{
\item \code{Hs} - simplified R over S approach
\item \code{Hrs} - corrected R over S Hurst exponent
\item \code{He} - empirical Hurst exponent
\item \code{Hal} - corrected empirical Hurst exponent
\item \code{Ht} - theoretical Hurst exponent
}
}
\note{
Derived from Matlab code of R. Weron, published on Matlab Central.
}
\references{
H.E. Hurst (1951) Long-term storage capacity of reservoirs, Transactions
of the American Society of Civil Engineers 116, 770-808.
R. Weron (2002) Estimating long range dependence: finite sample properties
and confidence intervals, Physica A 312, 285-299.
}
\seealso{
\code{fractal::hurstSpec, RoverS, hurstBlock} and \code{fArma::LrdModelling}
}
\examples{
## Computing the Hurst exponent
data(brown72)
x72 <- brown72 # H = 0.72
xgn <- rnorm(1024) # H = 0.50
xlm <- numeric(1024); xlm[1] <- 0.1 # H = 0.43
for (i in 2:1024) xlm[i] <- 4 * xlm[i-1] * (1 - xlm[i-1])
hurstexp(brown72, d = 128) # 0.72
# Simple R/S Hurst estimation: 0.6590931
# Corrected R over S Hurst exponent: 0.7384611
# Empirical Hurst exponent: 0.7068613
# Corrected empirical Hurst exponent: 0.6838251
# Theoretical Hurst exponent: 0.5294909
hurstexp(xgn) # 0.50
# Simple R/S Hurst estimation: 0.5518143
# Corrected R over S Hurst exponent: 0.5982146
# Empirical Hurst exponent: 0.6104621
# Corrected empirical Hurst exponent: 0.5690305
# Theoretical Hurst exponent: 0.5368124
hurstexp(xlm) # 0.43
# Simple R/S Hurst estimation: 0.4825898
# Corrected R over S Hurst exponent: 0.5067766
# Empirical Hurst exponent: 0.4869625
# Corrected empirical Hurst exponent: 0.4485892
# Theoretical Hurst exponent: 0.5368124
## Compare with other implementations
\dontrun{
library(fractal)
x <- x72
hurstSpec(x) # 0.776 # 0.720
RoverS(x) # 0.717
hurstBlock(x, method="aggAbs") # 0.648
hurstBlock(x, method="aggVar") # 0.613
hurstBlock(x, method="diffvar") # 0.714
hurstBlock(x, method="higuchi") # 1.001
x <- xgn
hurstSpec(x) # 0.538 # 0.500
RoverS(x) # 0.663
hurstBlock(x, method="aggAbs") # 0.463
hurstBlock(x, method="aggVar") # 0.430
hurstBlock(x, method="diffvar") # 0.471
hurstBlock(x, method="higuchi") # 0.574
x <- xlm
hurstSpec(x) # 0.478 # 0.430
RoverS(x) # 0.622
hurstBlock(x, method="aggAbs") # 0.316
hurstBlock(x, method="aggVar") # 0.279
hurstBlock(x, method="diffvar") # 0.547
hurstBlock(x, method="higuchi") # 0.998
}
}
\keyword{ timeseries }