assuming you pull the data you want into x and y:
w...@ubuntu:~$ R
> library(fts)
> x <- fts()
> y <- fts()
> xy.cor.200 <- moving.cor(x,y,200)
> tail(xy.cor.200)
[,1]
2012-03-12 -0.3009635
2012-03-13 -0.2923489
2012-03-14 -0.2824015
2012-03-15 -0.2662689
2012-03-16 -0.2566354
2012-03-17 -0.2537089
2012-03-18 -0.2490421
2012-03-19 -0.2391911
2012-03-20 -0.2263381
2012-03-21 -0.2113029
>
which is just using c++ to do the calculation.
here is the template function for correlation that fts uses:
http://github.com/armstrtw/tslib/blob/5b0fe2fc5ecb393d1dca097c2c19008227eb6c7e/tslib/vector.summary/cor.hpp
-Whit
On Fri, Jun 26, 2009 at 4:57 PM, Ted Byers<r.ted.by...@gmail.com> wrote:
> 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
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
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> and provide commented, minimal, self-contained, reproducible code.
>
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