FDSimulate {fractal} | R Documentation |
Creates a realization of a time-varying fractionally differenced (FD) process with a given vector of FD parameters and corresponding vector of innovations variances.
FDSimulate(delta, innovations.var=1, method="ce", seed=0)
delta |
a vector containing time-varying FD parameters. |
innovations.var |
a numeric vector or scalar containing (time-varying) FD innovations variances.
If a scalar, the value is replicated appropriately. Otherwise, the length of this input should match the
length of the delta vector. Default: 1 . |
method |
a character string defining the method to use in forming the FD realization.
Choices are "ce" (circulent emebdding) and "cholesky" . Default: "ce" . |
seed |
a positive integer representing the initial seed value to use
for the random number generator. If seed=0 , the current time is used
as a means of generating a (unique) seed value. Otherwise, the specified
seed value is used. Default: 0 . |
a vector containing a (time-varying) FD process realization corresponding to the input FD model parameters.
TRUE
.TRUE
.TRUE
.
D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, 2000.
D. B. Percival and W.L.B. Constantine, Exact Simulations of Time-Varying Fractionally Differenced Processes, submitted to Journal of Computational and Graphical Statistics, 2002.
FDWhittle
, wavFDPBlock
, wavFDPTime
.
## create a time-varying FD parameter, linearly ## varying from white to pink noise, then jump ## to a red noise plateau delta <- c(seq(0, 0.5, by=0.01), rep(1,100)) ## set the innovations variance to unity innovation <- rep(1, length(delta)) ## simulate a time-varying FD process z <- FDSimulate(delta=delta, innovation=innovation) print(z) plot(z)