🚀 go-pugleaf

how about independent components analysis---
which allows for nonlinear relationships---
just wondering....
"Arthur J. Kendall" <Arthur.Kendall@verizon.net> wrote in message
news:3D0748DB.E857635D@verizon.net...
> It seems that you want to reduce each time series to a a few parameters
and
> cluster those parameters.
>
> The Classification Society of North America page
> http://www.pitt.edu/~csna/
> might be of help.   I have seen this kind of problem at past CSNA
meetings.
> Browse the abstracts etc.
>
> You might post a message on <class-l> describing your data and the kind of
> questions you have.
>
>
> Eric Bohlman wrote:
>
> > In sci.stat.math Alexander Sirotkin <demiurg@tau.ac.il> wrote:
> > > There is an information in this data set that may current
> > > classification model does not use. The model does not take into
> > > account taht these 562 real predictor variables (not records !!!)
> > > have order, meaning that the variables are not just x[1], x[2],...,
x[561]
> > > but it is important that x[3] comes after x[2] (because these
variables
> > > are actually samples from continues function).
> >
> > > what I'm trying to do, actually, is to build a classification model
> > > using functions (sampled at 562 points) as independent variables !
> >
> > Ah, you're saying that your independent variables are actually the
values
> > of a time series.  In that case, some of your difficulty may be coming
> > from the fact that they're so highly correlated that it's difficult to
> > distinguish the weights that should be assigned them.
> >
> > It looks like you're really trying to estimate the coefficients of a
> > finite-impulse-response filter whose output is then somehow quantized
into
> > 80-something not-necessarily-equal-sized steps.  I'm not really sure how
> > to deal with such a problem.  One thing that's clear is that anything
> > resembling classical discriminant analysis isn't going to work with your
> > sample size of around 10,000, because it would require estimating around
> > 45,000 parameters which is almost 5 times the number of independent
> > observations you have.  I also have a very strong hunch that your
apparent
> > "success" with stepwise methods is actually success at fitting the
noise,
> > rather than signal, in your data.
>