Correlation Analysis function computes and plots both empirical ACF and PACF of univariate time series.

```
corr_analysis(
x,
lag.max = NULL,
type = "correlation",
demean = TRUE,
show.ci = TRUE,
alpha = 0.05,
plot = TRUE,
...
)
```

- x
A

`vector`

or`"ts"`

object (of length \(N > 1\)).- lag.max
A

`integer`

indicating the maximum lag up to which to compute the ACF and PACF functions.- type
A

`character`

string giving the type of acf to be computed. Allowed values are "correlation" (the default) and "covariance".- demean
A

`bool`

indicating whether the data should be detrended (`TRUE`

) or not (`FALSE`

). Defaults to`TRUE`

.- show.ci
A

`bool`

indicating whether to compute and show the confidence region. Defaults to`TRUE`

.- alpha
A

`double`

indicating the level of significance for the confidence interval. By default`alpha = 0.05`

which gives a 1 -`alpha`

= 0.95 confidence interval.- plot
A

`bool`

indicating whether a plot of the computed quantities should be produced. Defaults to`TRUE`

.- ...
Additional parameters.

Two `array`

objects (ACF and PACF) of dimension \(N \times S \times S\).

```
# Estimate both the ACF and PACF functions
corr_analysis(datasets::AirPassengers)
```