Auto-Covariance and Correlation Functions

Source: R/ACF.R

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

# Get Autocorrelation
m = ACF(datasets::AirPassengers)

# Get Autocovariance and do not remove trend from signal
m = ACF(datasets::AirPassengers, cor = FALSE, demean = FALSE)