🚀 go-pugleaf

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.