I suspect I'm looking in the wrong places, so guidance to the relevant
documentation would be as welcome as a little code snippet.

I have time series data stored in a MySQL database.  There is the usual DATE
field, along with a double precision number: there are daily values
(including only normal working days: Monday through Friday).  I actually
have to do a couple things here.  Because of how the result is to be used, I
need to first create two time series.  The first is the delta between 22
working days, and the second is the delta between 66 working days.  I have
hundreds of these datasets, and some go back 30 years.  I need to estimate
the correlation between 22 day deltas (i.e. is the delta for one month
correlated with that of the previous month) and between the 22 day delta and
the 66 day delta that ends the day before the the first day of the 22 day
delta.  However, I KNOW the statistical properties of the time series are
not constant (so the usual assumptions do not apply to the entire series).
Therefore, I want to subsample finely enough to get a reasonably sensible
correlation and examine how that changes through time.  (There are no tests
of significance here: I just want to explore just how much the properties of
these series change through time).

I have C++ code, admittedly not written particularly efficiently, that does
this.  The question is, is it possible to do this reasonably efficiently
using R?

Thanks

Ted

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