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, ... )
"ts" object (of length \(N > 1\)).
integer indicating the maximum lag up to which to compute the ACF and PACF functions.
character string giving the type of acf to be computed. Allowed values are "correlation" (the default) and "covariance".
bool indicating whether the data should be detrended (
TRUE) or not (
FALSE). Defaults to
bool indicating whether to compute and show the confidence region. Defaults to
double indicating the level of significance for the confidence interval. By default
alpha = 0.05 which gives a 1 -
alpha = 0.95 confidence interval.
bool indicating whether a plot of the computed quantities should be produced. Defaults to
array objects (ACF and PACF) of dimension \(N \times S \times S\).
# Estimate both the ACF and PACF functions corr_analysis(datasets::AirPassengers)