I have a question using R's ccf() function. I have two time series that represents snow water equivalent on the surface and groundwater head under the ground. I want to find out the "propagation" time from the surface to the ground, so I think that using cross correlation between two time series can help me to detect what's the "lag" time between them.
It seems that ccf() function is a proper way to determine the lag of two time series. But according to the mathematical concepts of cross correlation, it seems that it requires stationarity of the input data, and both of my time series are seasonal, because intuitively we know that snow occurs in winter. Data with seasonality is considered as non-stationary, so I think I might need to decompose the data so that it's stationary. Then I used both stl() function and decompose() function to detect whether there is a seasonality pattern, but both of them gave me this error message:
Error in decompose(swefoothill):
time series has no or less than 2 periods
which is pretty self explanatory, both time series don't have a clear seasonality. But that doesn't mean that my data are not seasonal. So I want to ask under this circumstance, is it okay for me to perform a ccf() directly for both time series? I did a sample analysis and the correlation factor figure looks like this:
And I'm observing a cycle pattern here, am I doing it wrong? Thanks a lot for your help!