[R] Cross correlation in time series

[Hufkens Koen]

Hi list,

I'm working on time series of (bio)physical data explaining (or not) the
net ecosystem exchange of a system (+_ CO2 in versus CO2 out balance).

I decomposed the time series of the various explaining variable
according to scale (wavelet decomposition). With the coefficients I got
from the wavelet decomposition I applied a (multiple) regression, giving
some expected results. The net ecosystem exchange (CO2 balance) is
mostly determined by the light regime (driving photosynthesis) and the
water availability (inducing stress if absent), and this over all the
scales.

So sadly, pinpointing a certain scale on a certain process wasn't
possible. I had hoped to see for example a relation between air
temperature and a response of the vegetation/ecosystem, and this for a
certain scale. Global trends are present but short term responses are
not present.

Using regressions in this previous analysis I thought that considering
that some processes do show some lag if it comes to showing a response
on a changing variable a one on one regression of time series might have
been the wrong approach because this states that there is also an almost
direct one in one relation between action and reaction. So what I'm
looking for is a method to determine that after let's say 5 warm days,
the net ecosystem exchange peaks as well.

Any suggestions on how to determine a certain, lag between time series.
I found a post in the archives discussing convolve() but this doesn't
seem the right thing. Also ccf() is mentioned, does this calculate a
correlation coefficient as two time series are shifted past eachother
for certain lag distances or am I mistaken? If so, what is the
implication of using a smaller and smaller sampling window (increasing
your time resolution say going from year level to month to day level)?

Any input on how to compare two time series would be appreciated...

Sorry for the rather theoretical/technical post.
Best regards,
Koen Hufkens