Description Usage Arguments Value References Examples
rARMA
generates d
dimensional time series observations from a vARMA(2,2)
(vectorautoregressivemovingaverage) process based on Gaussian white noise for testing and simulation
purposes.
1 
n 
number of time series observations to be generated. 
d 
dimension of the multivariate time series. 
Phi 
a (d, d, 2)dimensional array, with 
Theta 
a (d, d, 2)dimensional array, with 
Sigma 
the covariance matrix of the Gaussian white noise component. 
burn 
a burnin period when generating the time series observations, by default 
freq 
an optional vector of frequencies, if 
The function returns a list with two components:

generated time series observations, the 

if 
BD06pdSpecEst
1 2 3 4 5 6 7  ## ARMA(1,1) process: Example 11.4.1 in (Brockwell and Davis, 1991)
freq < seq(from = pi / 100, to = pi, length = 100)
Phi < array(c(0.7, 0, 0, 0.6, rep(0, 4)), dim = c(2, 2, 2))
Theta < array(c(0.5, 0.7, 0.6, 0.8, rep(0, 4)), dim = c(2, 2, 2))
Sigma < matrix(c(1, 0.71, 0.71, 2), nrow = 2)
ts.sim < rARMA(200, 2, Phi, Theta, Sigma, freq = freq)
ts.plot(ts.sim$X) # plot generated time series traces.

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