# Cross-correlation gives autocorrelation in R?

I am not very familiar with cross-correlation analysis. I am analysing a large number of "paired" time series. For each pair, I am doing a ccf in R. I read in other posts that the horizontal line given by R indicates the significant values. How are they tested to be significant? Because I have to do this for hundreds of pairs, is there a way of grabbing the significant number from the R output?

> print(ccf(x,y))
Autocorrelations of series ‘X’, by lag
-6     -5     -4     -3     -2     -1      0      1      2      3      4
-0.242 -0.090  0.057  0.197  0.466  0.699  0.896  0.436  0.221 -0.018 -0.116


Related to the R output, I noticed that these values are called autocorrelation coefficients? But they describe the correlation of a lagged A series with series B right? Does anybody have any more insight into why it's called autocorrelation?

1. Yes, the values you get describe the correlation of lagged A series (or x in your code) with series B (or y in your code).
2. It is called autocorrelation, because cross-correlations of series A, and B, come from auto-correlation of multivariate time series $(A,B)$. For univariate time series auto-correlation is scalar, for multivariate it is matrix. It is still auto, meaning that it is correlation with itself.
3. If you only need the values, use plot = FALSE in the call to ccf.