hurst.est: Estimate Hurst Exponent

In Rwave: Time-Frequency Analysis of 1-D Signals

Estimate Hurst Exponent

Description

Estimates Hurst exponent from a wavelet transform.

Usage

hurst.est(wspec, range, nvoice, plot=TRUE)

Arguments

wspec	wavelet spectrum (output of tfmean)
range	range of scales from which estimate the exponent.
nvoice	number of scales per octave of the wavelet transform.
plot	if set to TRUE, displays regression line on current plot.

Value

complex 1D array of size sigsize.

References

See discussions in the text of “Practical Time-Frequency Analysis”.

See Also

tfmean, wspec.pl.

Examples

# White Noise Hurst Exponent: The plots on the top row of Figure 6.8
# were produced by the folling S-commands. These make use of the two
# functions Hurst.est (estimation of Hurst exponent from CWT) and
# wspec.pl (display wavelet spectrum).

# Compare the periodogram and the wavelet spectral estimate.
wnoise <- rnorm(8192)
plot.ts(wnoise)
spwnoise <- fft(wnoise)
spwnoise <- Mod(spwnoise)
spwnoise <- spwnoise*spwnoise
plot(spwnoise[1:4096], log="xy", type="l")
lswnoise <- lsfit(log10(1:4096), log10(spwnoise[1:4096]))
abline(lswnoise$coef)
cwtwnoise <- DOG(wnoise, 10, 5, 1, plot=FALSE)
mcwtwnoise <- Mod(cwtwnoise)
mcwtwnoise <- mcwtwnoise*mcwtwnoise
wspwnoise <- tfmean(mcwtwnoise, plot=FALSE)
wspec.pl(wspwnoise, 5)
hurst.est(wspwnoise, 1:50, 5)

Rwave documentation built on Oct. 22, 2022, 1:05 a.m.