simGauss {longmemo} | R Documentation |
Simulation of a Gaussian series X(1), ..., X(n). Whereas
simGauss
works from autocovariances, the others call it,
for simulating a fractional ARIMA(0,d,0) process (d = H-1/2),
or fractional Gaussian noise, respectively.
simARMA0(n, H) simFGN0 (n, H) simGauss(autocov)
n |
length of time series |
H |
self-similarity parameter |
autocov |
numeric vector of auto covariances gamma(0), ..., gamma(n-1). |
simGauss
implements the method by Davies and Harte which is
relatively fast using the FFT (fft
) twice.
To simulate ARIMA(p, d, q), (for d in (-1/2, 1,2), you can use
arima.sim(n, model = list(ar= .., ma = ..),
innov= simARMA0(n,H=d+1/2) , n.start = 0)
.
The simulated series X(1), ..., X(n), an R object of class
"ts"
, constructed from ts()
.
Jan Beran (original) and Martin Maechler (simGauss
,
speedup, simplication)).
Beran (1994), 11.3.3, p.216~f, referring to
Davis, R.B. and Harte, D.S. (1987). Tests for Hurst effect, Biometrika 74, 95–102.
ckARMA0
on which simARMA0
relies, and
ckFGN0
on which simFGN0
relies.
x1 <- simFGN0(100, 0.7) x2 <- simARMA0(100, 0.7) plot(simFGN0(1000, 0.8)) #- time series plot