# Acf

From forecast v5.7
by Rob Hyndman

##### (Partial) Autocorrelation Function Estimation

The function `Acf`

computes (and by default plots) an estimate of the autocorrelation function of a univariate time series. Function `Pacf`

computes (and by default plots) an estimate of the partial autocorrelation function of a univariate time series. These improve the `acf`

and `pacf`

functions when applied to univariate time series.
The main differences are that `Acf`

does not plot a spike at lag 0 (which is redundant)
and the horizontal axes show lags in time units rather than seasonal units.

- Keywords
- ts

##### Usage

```
Acf(x, lag.max=NULL, type=c("correlation", "partial"),
plot=TRUE, main=NULL, ylim=NULL, na.action=na.contiguous, ...)
Pacf(x, main=NULL, ...)
```

##### Arguments

- x
- a univariate time series
- lag.max
- maximum lag at which to calculate the acf. Default is 10*log10(N/m) where N is the number of observations and m the number of series. Will be automatically limited to one less than the number of observations in the series.
- type
- character string giving the type of acf to be computed. Allowed values are "
`correlation`

" (the default) or "`partial`

". - plot
- logical. If TRUE (the default) the acf is plotted.
- main
- Title for plot
- ylim
- The y limits of the plot
- na.action
- function to handle missing values. Default is
`na.contiguous`

. Useful alternatives are`na.pass`

and`na.`

- ...
- Additional arguments passed to
`acf`

.

##### Details

See the `acf`

function in the stats package.

##### Value

- See the
`acf`

function in the stats package.

##### See Also

##### Examples

```
Acf(wineind)
Pacf(wineind)
```

*Documentation reproduced from package forecast, version 5.7, License: GPL (>= 2)*

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