The ACF function computes the estimated
autocovariance or autocorrelation for both univariate and multivariate cases.

ACF(x, lagmax = 0, cor = TRUE, demean = TRUE)

## Arguments

x |
A `matrix` with dimensions \(N \times S\) or N observations and S processes |

lagmax |
A `integer` indicating the max lag. |

cor |
A `bool` indicating whether the correlation
(`TRUE` ) or covariance (`FALSE` ) should be computed. |

demean |
A `bool` indicating whether the data should be detrended
(`TRUE` ) or not (`FALSE` ) |

## Value

An `array`

of dimensions \(N \times S \times S\).

## Details

`lagmax`

default is \(10*log10(N/m)\) where \(N\) is the number of
observations and \(m\) is the number of series being compared. If
`lagmax`

supplied is greater than the number of observations, then one
less than the total will be taken.

## Examples