Description Usage Arguments Details Value Examples

Simulation of a stationary Gaussian time series.

1 |

`n` |
Number of time series to generate. |

`acf` |
Length- |

`Z` |
Optional size |

`fft` |
Logical, whether or not to use the superfast FFT-based algorithm of Chan and Wood or the more stable Durbin-Levinson algorithm. See Details. |

`nkeep` |
Length of time series. Defaults to |

`tol` |
Relative tolerance on negative eigenvalues. See Details. |

The superfast method fails when the circulant matrix is not positive definite. This is typically due to one of two things, for which the FFT algorithm can be tuned with `tol`

and `nkeep`

:

`tol`

Roundoff error can make tiny eigenvalues appear negative. If

`evMax`

is the maximum eigenvalue, then all negative eigenvalues of magnitude less than`tol * evMax`

are mapped to this threshold value. This does not guarantee a positive definite embedding.`nkeep`

The autocorrelation is decaying too slowly on the given timescale. To mitigate this it is possible to increase the time horizon, i.e. input a longer

`acf`

and keep the first`nkeep`

time series observations. For consistency,`nkeep`

also applies to Durbin-Levinson method.

Length-`nkeep`

vector or size `nkeep x n`

matrix with time series as columns.

1 2 3 |

SuperGauss documentation built on May 1, 2019, 7:58 p.m.

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