Description Usage Arguments Details Value Author(s) References See Also Examples
The function 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.
1 2 3 4 5 6 7 8 9 10 | 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, demean=TRUE, ...)
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, ...)
|
x |
a univariate or multivariate (not Ccf) numeric time series object or a numeric vector or matrix. |
y |
a univariate numeric time series object or a numeric vector. |
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
" |
plot |
logical. If |
na.action |
function to handle missing values. Default is |
demean |
Should covariances be about the sample means? |
calc.ci |
If |
level |
Percentage level used for the confidence intervals. |
nsim |
The number of bootstrap samples used in estimating the confidence intervals. |
... |
Additional arguments passed to the plotting function. |
The functions improve the acf
, pacf
and 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).
The Acf
, Pacf
and Ccf
functions return objects of class "acf" as described in acf
from the stats package. The taperedacf
and taperedpacf
functions return objects of class "mpacf".
Rob J Hyndman
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.
1 2 3 4 5 6 7 | Acf(wineind)
Pacf(wineind)
## Not run:
taperedacf(wineind, nsim=50)
taperedpacf(wineind, nsim=50)
## End(Not run)
|
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