| LongMemoryModelling {fSeries} | R Documentation |
A collection and description of functions to investigate the
long memory behavior of an univariate time series process.
The functions and methods are:
fgnSim | Simulates Fractional Gaussian Noise. |
Sorry, not yet implemented are the functions to fit the Hurst exponent.
fgnSim(n = 1000, H = 0.7, method = c("beran", "durbin", "paxson"),
mean = 0, std = 1)
H |
the Hurst exponent, a numeric value between 0.5 and 1, by default 0.7. |
mean, std |
mean and standard deviation of the random innovations. By default a zero mean and a unit standard deviation is assumed. |
method |
the method how to generate the time series sequence, one
of the following character strings: "beran",
"durbin", or "paxson".
|
n |
number of data points to be simulated, a numeric value, by default 1000. |
returns a numeric vector of length n, the fractional
Gaussian noise series.
Paxson V. (1995); Fast Approximation of Self-Similar Network Traffic, Berkeley.
## fgnSim -
par(mfrow = c(3, 1), cex = 0.75)
# Beran's Method:
plot(fgnSim(n = 200, H = 0.75), type = "l",
ylim = c(-3, 3), xlab = "time", ylab = "x(t)", main = "Beran")
# Durbin's Method:
plot(fgnSim(n = 200, H = 0.75, method = "durbin"), type = "l",
ylim = c(-3, 3), xlab = "time", ylab = "x(t)", main = "Durbin")
# Paxson's Method:
plot(fgnSim(n = 200, H = 0.75, method = "paxson"), type = "l",
ylim = c(-3, 3), xlab = "time", ylab = "x(t)", main = "Paxson")