Generate a random time series
Generate a random time series (matrix). This is a utility typically used in a time series model simulate method and not called directly by the user.
makeTSnoise(sampleT,p,lags,noise=NULL, rng=NULL, Cov=NULL, sd=1,
noise.model=NULL, noise.baseline=0,
tf=NULL, start=NULL,frequency=NULL)sampleT |
an integer indicating the number of periods. |
p |
an integer indicating the number of series. |
lags |
an integer indicating the number of periods prior to the sample (initial data w0) for which random numbers should be generated. This is useful in ARMA models. |
noise |
Noise can be supplied. Otherwise it will be generated. If supplied it should be a list as described below under returned value. |
Cov |
The covariance of the noise process. If this is specified then sd
is ignored. A vector or scalar is treated as a diagonal matrix. For an object of class
TSestModel, if neither Cov nor sd are specified, then Cov is set to
the estimated covariance ( |
sd |
The standard deviation of the noise. This can be a vector. |
noise.model |
A TSmodel to be used for generating noise (not yet supported by SS methods). |
noise.baseline |
a constant or matrix to be added to noise. Alternately this can be a vector of length p, each value of which is treated as a constant to add to the coresponding noise series. |
rng |
The random number generator information needed to regenerate a simulation. |
tf |
a time frame to use for the generated matrix. (alternately use start and frequency) |
start |
a time start date to use for the generated matrix. |
frequency |
a time frequency to use for the generated matrix. |
A time series matrix.
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