On Wed, 27 Jun 2012, Cand2d wrote:
Hi to all,
I am trying to run breakpoints() on a fairly large sample (>10.000
observations). The process is very slow, any idea on how to speed this
up? I have tried the hpc="foreach" parameter, but this didn't work at
all when I tried to run it on a smaller sample.
The dynamic programming algorithm implemented in breakpoints() has
computations and storage requirements of order O(n^2). And it is
implemented in pure R. So with n > 10,000 you would need to have loads of
cores to bring it to something computable. And even then, you would
probably have to wait very long.
There are more efficient implementations of the dynamic programming
algorithm available, however, I think only for the simple location model
(x ~ 1) and not for general regressions. Similarly, alternative algorithms
(such as PELT in package "changepoints" which is of order O(n)) are not
available for general regressions to the best of my knowledge.
Either you could try recursive splitting of the data. Or you could coarsen
the time grid...
hth,
Z
breakpoints(x ~ x.l1 + x.l2 + X.l3 + x.l4 + x.l5 + x.l6 + x.l7 + x.l8 + y.l1
+ y.l2 + y.l3 + y.l4 + y.l5 + y.l6 + y.l7 + y.l8 + index(x))
Thanks.
Regards
Christoph
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