SimulateFGN: Simulates FGN
In FGN: Fractional Gaussian Noise and power law decay time series model fitting

Description Usage Arguments Details Value Author(s) References See Also Examples

A fractional Gaussian noise time series is simulated.

1	SimulateFGN(n, H)

`n`	length of time series
`H`	Hurst coefficient

The FFT is used so it is most efficient if you select n to be a power of 2.

vector of length containing the simulated time series

A.I. McLeod

Davies, R. B. and Harte, D. S. (1987). Tests for Hurst Effect. Biometrika 74, 95–101.

McLeod, A.I., Yu, Hao, Krougly, Zinovi L. (2007). Algorithms for Linear Time Series Analysis, Journal of Statistical Software.

DLSimulate

#Example 1
#simulate a process with H=0.2 and plot it
z<-SimulateFGN(100, 0.2)
ts.plot(z)
# 
#Example 2
#simulate FGN and compare theoretical and sample autocovariances
H<-0.7
n<-8192
z<-SimulateFGN(n, H)
#autocovariances
sacvf<-acf(z, plot=FALSE,type="covariance")$acf
tacf<-acvfFGN(H, n-1)
tb<-matrix(c(tacf[1:10],sacvf[1:10]),ncol=2)
dimnames(tb)<-list(0:9, c("Tacvf","Sacvf"))
tb