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 tibble 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 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).
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.
#> #>#>#> #>#>#> #>#>#> #>aus_elec %>% ACF(Temperature)#> # A tsibble: 235 x 3 [30m] #> # Key: State [5] #> State lag acf #> <chr> <lag> <dbl> #> 1 New South Wales 30m 0.992 #> 2 New South Wales 60m 0.978 #> 3 New South Wales 90m 0.959 #> 4 New South Wales 120m 0.935 #> 5 New South Wales 150m 0.908 #> 6 New South Wales 180m 0.878 #> 7 New South Wales 210m 0.847 #> 8 New South Wales 240m 0.814 #> 9 New South Wales 270m 0.781 #> 10 New South Wales 300m 0.748 #> # … with 225 more rowsaus_elec %>% PACF(Temperature)#> # A tsibble: 235 x 3 [30m] #> # Key: State [5] #> State lag pacf #> <chr> <lag> <dbl> #> 1 New South Wales 30m 0.992 #> 2 New South Wales 60m 0.444 #> 3 New South Wales 90m 0.202 #> 4 New South Wales 120m 0.116 #> 5 New South Wales 150m 0.0657 #> 6 New South Wales 180m 0.0309 #> 7 New South Wales 210m 0.000787 #> 8 New South Wales 240m 0.0180 #> 9 New South Wales 270m 0.0102 #> 10 New South Wales 300m 0.0179 #> # … with 225 more rows#> # 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