ACF.Rd
The function ACF
computes an estimate of the autocorrelation function
of a (possibly multivariate) tsibble. Function PACF
computes an estimate
of the partial autocorrelation function of a (possibly multivariate) tsibble.
Function CCF
computes the crosscorrelation or crosscovariance of two columns
from a tsibble.
ACF(.data, ..., lag_max = NULL, demean = TRUE, type = c("correlation", "covariance", "partial")) PACF(.data, ..., lag_max = NULL) CCF(.data, ..., lag_max = NULL, type = c("correlation", "covariance"))
.data  A tsibble 

...  The column(s) from the tsibble used to compute the ACF, PACF or CCF. 
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. 
demean  logical. Should the covariances be about the sample means? 
type  character string giving the type of acf to be computed.
Allowed values are

The ACF
, PACF
and CCF
functions return objects
of class "tbl_cf", which is a tsibble containing the correlations computed.
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 resulting tables from these functions can also be plotted using
autoplot.tbl_cf()
.
Hyndman, R.J. (2015). Discussion of ``Highdimensional autocovariance matrices and optimal linear prediction''. Electronic Journal of Statistics, 9, 792796.
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), 471482.
#> #>#>#> #>#>#> #>#>#> #>vic_elec %>% ACF(Temperature)#> # A tsibble: 47 x 2 [30m] #> lag acf #> <lag> <dbl> #> 1 30m 0.994 #> 2 60m 0.982 #> 3 90m 0.967 #> 4 120m 0.948 #> 5 150m 0.925 #> 6 180m 0.901 #> 7 210m 0.873 #> 8 240m 0.845 #> 9 270m 0.815 #> 10 300m 0.785 #> # … with 37 more rowsvic_elec %>% ACF(Temperature) %>% autoplot()vic_elec %>% PACF(Temperature)#> # A tsibble: 47 x 2 [30m] #> lag pacf #> <lag> <dbl> #> 1 30m 0.994 #> 2 60m 0.395 #> 3 90m 0.220 #> 4 120m 0.141 #> 5 150m 0.0911 #> 6 180m 0.0610 #> 7 210m 0.0252 #> 8 240m 0.0101 #> 9 270m 0.0151 #> 10 300m 0.0170 #> # … with 37 more rowsvic_elec %>% PACF(Temperature) %>% autoplot()#> # A tsibble: 29 x 3 [1Y] #> # Key: Country [1] #> Country lag ccf #> <fct> <lag> <dbl> #> 1 Australia 14Y 0.0315 #> 2 Australia 13Y 0.0673 #> 3 Australia 12Y 0.108 #> 4 Australia 11Y 0.152 #> 5 Australia 10Y 0.203 #> 6 Australia 9Y 0.268 #> 7 Australia 8Y 0.321 #> 8 Australia 7Y 0.389 #> 9 Australia 6Y 0.472 #> 10 Australia 5Y 0.563 #> # … with 19 more rows