🚀 go-pugleaf

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.