(Partial) Autocorrelation and Cross-Correlation Function Estimation
Acf computes (and by default plots) an estimate of the autocorrelation function of a (possibly multivariate) time series. Function
Pacf computes (and by default plots) an estimate of the partial autocorrelation function of a (possibly multivariate) time series. Function
Ccf computes the cross-correlation or cross-covariance of two univariate series.
Acf(x, lag.max = NULL, type = c("correlation", "covariance", "partial"), plot = TRUE, na.action = na.contiguous, demean=TRUE, ...) Pacf(x, lag.max=NULL, plot=TRUE, na.action=na.contiguous, ...) Ccf(x, y, lag.max=NULL, type=c("correlation","covariance"), plot=TRUE, na.action=na.contiguous, ...) taperedacf(x, lag.max=NULL, type=c("correlation", "partial"), plot=TRUE, calc.ci=TRUE, level=95, nsim=100, ...) taperedpacf(x, ...)
- a univariate or multivariate (not Ccf) numeric time series object or a numeric vector or matrix.
- a univariate numeric time series object or a numeric vector.
- 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.
- character string giving the type of acf to be computed. Allowed values are
correlation" (the default), "
covariance" or "
- logical. If
TRUE(the default) the resulting acf, pacf or ccf is plotted.
- function to handle missing values. Default is
na.contiguous. Useful alternatives are
- Should covariances be about the sample means?
TRUE, confidence intervals for the ACF/PACF estimates are calculated.
- Percentage level used for the confidence intervals.
- The number of bootstrap samples used in estimating the confidence intervals.
- Additional arguments passed to the plotting function.
The functions improve the
ccf functions. The main differences are that
Acf does not plot a spike at lag 0 when
type=="correlation" (which is redundant) and the horizontal axes show lags in time units rather than seasonal units.
The tapered versions implement the ACF and PACF estimates and plots described in Hyndman (2015), based on the banded and tapered estimates of autocovariance proposed by McMurry and Politis (2010).
Ccffunctions return objects of class "acf" as described in
acffrom the stats package. The
taperedpacffunctions return objects of class "mpacf".
Hyndman, R.J. (2015). Discussion of ``High-dimensional autocovariance matrices and optimal linear prediction''. Electronic Journal of Statistics, 9, 792-796.
McMurry, T. L., & Politis, D. N. (2010). Banded and tapered estimates for autocovariance matrices and the linear process bootstrap. Journal of Time Series Analysis, 31(6), 471-482.
Acf(wineind) Pacf(wineind) ## Not run: # taperedacf(wineind, nsim=50) # taperedpacf(wineind, nsim=50) # ## End(Not run)