hosking.sim: Generate Stationary Gaussian Process Using Hosking's Method

In waveslim: Basic Wavelet Routines for One-, Two-, and Three-Dimensional Signal Processing

Generate Stationary Gaussian Process Using Hosking's Method

Description

Uses exact time-domain method from Hosking (1984) to generate a simulated time series from a specified autocovariance sequence.

Usage

hosking.sim(n, acvs)

Arguments

n	Length of series.
acvs	Autocovariance sequence of series with which to generate, must be of length at least n.

Value

Length n time series from true autocovariance sequence acvs.

Author(s)

B. Whitcher

References

Hosking, J. R. M. (1984) Modeling persistence in hydrological time series using fractional differencing, Water Resources Research, 20, No. 12, 1898-1908.

Percival, D. B. (1992) Simulating Gaussian random processes with specified spectra, Computing Science and Statistics, 22, 534-538.

Examples

dB <- function(x) 10 * log10(x)
per <- function (z) {
  n <- length(z)
  (Mod(fft(z))^2/(2 * pi * n))[1:(n%/%2 + 1)]
}
spp.sdf <- function(freq, delta, omega)
  abs(2 * (cos(2*pi*freq) - cos(2*pi*omega)))^(-2*delta)
data(acvs.andel8)
n <- 1024
## Not run: 
z <- hosking.sim(n, acvs.andel8[,2])
per.z <- 2 * pi * per(z)
par(mfrow=c(2,1), las=1)
plot.ts(z, ylab="", main="Realization of a Seasonal Long-Memory Process")
plot(0:(n/2)/n, dB(per.z), type="l", xlab="Frequency", ylab="dB",
     main="Periodogram")
lines(0:(n/2)/n, dB(spp.sdf(0:(n/2)/n, .4, 1/12)), col=2)

## End(Not run)

waveslim documentation built on Aug. 14, 2022, 5:07 p.m.