Graham
As Sujit said, you are looking for a multiple OLS regression  there
are literally tons of references on the web and elsewhere. A nice book
that is a reasonably gentle intro (which I think is what you want) is
something like "Statistics And Finance" by David Ruppert.
 Rory
On 13 Jul 2009, at 19:37, Graham Smith wrote:
> Thanks guys that's really useful and tells me I'm at least looking
> in the
> right area. I understand some of what you are talking about but the
> rest
> leaves me scratching my head in bewilderment. Do you happen to know
> of any
> good sites where I could learn about this? Many years ago I did
> advanced
> mathematics but that was focused on engineering rather than
> statistics so
> the ideas aren't completely alien to me.
>
> The solution I have at the moment gives really poor results so
> throwing
> polynomials at it would probably be an improvement but I understand
> what you
> are saying about over fitting to noise.
>
> 2009/7/13 Ted Dunning <ted.dunning@gmail.com>
>
>> 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 longterm exponential trend. This
>> is
>> probably best done using something like lowess smoothing. If you are
>> looking at longterm 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
>> overfitting 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
>>> commonsmath docs.
>>>
>>>
>> http://commons.apache.org/math/userguide/stat.html#a1.5_Multiple_linear_regression
>>>
>>> HTH
>>> Sujit
>>>
>>> On Mon, 20090713 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^n1 ....
>>>> 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
>> 8584140013 (m)
>> 4087730220 (fax)
>>

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