Create a simulation of a stochastic fractal time series according to a specfied model.
1 2 3  lmSimulate(x, sampling.interval=1,
mean=0, n.sample=128, generate.Sj=FALSE,
Sj=NULL, rn=NULL)

x 
an object of class 
Sj 
a numeric vector of DaviesHarte frequency
domain weights used to create the simulation(s). These weights
are calculated if not supplied. Default: 
generate.Sj 
a logical value. If 
mean 
the mean value of of the resulting simulation. Default: 
n.sample 
length of a time series.
Default: 
rn 
a vector of random normal deviates used to generate
uncorrelated random variables for the DaviesHarte simulator.
Default: 
sampling.interval 
the sampling interval for the process.
The SDF is computed for frequencies on the interval [0, Nyquist]
where Nyquist is 
Simulates a stochastic fractal time series via the DaviesHarte technique, which randomizes spectral weights and inverts the result back to the time domain. See the references for more details.
an object of class signalSeries
containing the simulated series.
D. Percival and A. Walden (2000), Wavelet Methods for Time Series Analysis, Cambridge University Press, Chapter 7.
J. Beran (1994), Statistics for LongMemory Processes, Chapman and Hall, Chapter 2.
D. Percival and A. Walden (1993), Spectral Analysis for Physical Applications, Cambridge University Press, 1993, Chapter 9.
Davies,R.B.and Harte,D.S.(1987). Tests for the Hurst effect, Biometrika, 74, 95–102.
lmModel
, lmACF
, lmSDF
, lmConfidence
, FDSimulate
.
1 2 3 4 5 6 7 8 
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