makeTSnoise {tframe} | R Documentation

## Generate a random time series

### Description

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.

### Usage

        makeTSnoise(sampleT,p,lags,noise=NULL, rng=NULL,
                 SIGMA=NULL, sd=1, noise.model=NULL, noise.baseline=0,
                 tf=NULL, start=NULL,frequency=NULL)

### Arguments

`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.
`SIGMA` | 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 SIGMA nor sd are specified, then SIGMA is set to the estimated covariance (`model$estimates$cov`).
`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.

### Value

A time series matrix.

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