In sci.nonlinear Karl <karlknoblich at yahoo.de> wrote:
> I want to calculate the correlation dimension of a time serie.
> What I have done
> I calculated the correlation integral C(r) (number of point having a
> distance smaller than r) for different embedding dimensions. Taking
> the slopes of the curve of log C(r) against log r for the different
> embedding dimensions and plotting them against the embedding dimension
> should result in a limes of the slopes: the correlation dimension.
> My problem
> Which slope shall I take?
> In examples I saw in text books there is a nice limit of the slopes
> with higher embedding dimensions. In my data I do not know which slope
> I should take because the slope of the curve varies. If I take the
> slope at a certain value of log r I can not get a limes.
> My curves (log C(r) against log r) can be seen in
> What to do? Does anybody knows such data and how to handle it?
> Hope somebody can help!
There is no guarantee that the limit exists.
There may be different slopes on different scales with different widths.
There is a huge difference between real life data and solution of
low dim mathematical models without noise (as seen in books).
Math Dept, Prague Institute of Chemical Technology