esmtools.stats.autocorr¶
-
esmtools.stats.
autocorr
(ds, dim='time', nlags=None)[source]¶ Compute the autocorrelation function of a time series to a specific lag.
Note
The correlation coefficients presented here are from the lagged cross correlation of
ds
with itself. This means that the correlation coefficients are normalized by the variance contained in the sub-series ofx
. This is opposed to a true ACF, which uses the entire series’ to compute the variance. See https://stackoverflow.com/questions/36038927/ whats-the-difference-between-pandas-acf-and-statsmodel-acfParameters: Returns: Dataset or DataArray with ACF results.