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From: Ted Dunning <ted.dunning@gmail.com>
Date: Mon, 13 Jul 2009 10:45:31 -0700
Message-ID: <c7d45fc70907131045j3d29fdf1v7f878ae4fb3a821@mail.gmail.com>
Subject: Re: Basic Maths Help
To: Commons Users List <user@commons.apache.org>, sujit.pal@comcast.net
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And if you are really working on time series for stocks, you will likely
have explosively bad results applying a simple polynomial fit.

You should, at least, remove the long-term exponential trend.  This is
probably best done using something like lowess smoothing.  If you are
looking at long-term data, you should also rescale as a percentage of long
term trend.

Then for modeling the data, you have to be very careful to avoid
over-fitting to noise.  Simply throwing polynomials at the problem is the
road to ruin.  Without significant math skills it will be difficult to get
really good results.  You might try penalizing your fit by also minimizing
the summed squares of your coefficients.   This is equivalent to weight
decay in neural networks.

Commons math is probably a very nice way to implement such algorithms in
production.  For exploratory development, I would recommend R instead.

On Mon, Jul 13, 2009 at 10:26 AM, Sujit Pal <sujit.pal@comcast.net> wrote:

> Hi Graham,
>
> You want multiple linear regression. Check out this page from the
> commons-math docs.
>
> http://commons.apache.org/math/userguide/stat.html#a1.5_Multiple_linear_regression
>
> HTH
> Sujit
>
> On Mon, 2009-07-13 at 17:25 +0100, Graham Smith wrote:
> > Hi,
> >
> > I'm hoping that someone with a bit more maths knowledge than I have can
> help
> > me with my current problem. I've got a data set that contains the daily
> > closing price for a number of different stocks. What I want to do is find
> an
> > equation that fits those points and then use it to predict the future
> price.
> >
> > In the past I've written an application that did a simple least squares
> > linear regression (what is handled by the SimpleRegression class I
> believe)
> > e.g. finding a line of best fit with the formula y = mx + c. What I need
> now
> > is something that can give me a formula of y = ax^n + bx^n-1 .... mx + c
> > where I can choose n, the number of terms.
> >
> > I think this can be handled by general least squares but the simple case
> I
> > implemented in the past was already pushing my understanding of maths. Is
> > this what the GLSMultipleLinearRegression class does? If so what do I
> need
> > to read up on to understand it?
> >
> > Many thanks,
> > Graham
>
>
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-- 
Ted Dunning, CTO
DeepDyve

111 West Evelyn Ave. Ste. 202
Sunnyvale, CA 94086
http://www.deepdyve.com
858-414-0013 (m)
408-773-0220 (fax)

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