| Computers Forum Index » Computer Artificial Intelligence - Neural Nets » Finding pattern in mostly randomized data... |
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 Posted: Thu Sep 24, 2009 8:05 pm |
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Guest
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Hello,
NN is a fine tool for extracting various pattern. Of course that
requires the existence of a pattern. But what to do if patterns are
rarely found or if there is a very blurred pattern structure.
I think this applies to data whith a considerably random-like origin
(for example: stock markets, results of sportevents).
In this context: Wich kind of neural Network (MLP, RBF, ...) should
be used and what should be avoided?
Alea |
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| Ian Parker... |
| Posted: Sun Sep 27, 2009 10:50 am |
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Guest
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On Sep 24, 9:05 pm, p... at (no spam) couptreffer.de wrote:
Quote: Hello,
NN is a fine tool for extracting various pattern. Of course that
requires the existence of a pattern. But what to do if patterns are
rarely found or if there is a very blurred pattern structure.
I think this applies to data whith a considerably random-like origin
(for example: stock markets, results of sportevents).
In this context: Wich kind of neural Network (MLP, RBF, ...) should
be used and what should be avoided?
Alea
With random data the normal approach is to place all the stock prices
into a vector and construct a matrix with the stock market prices
against time. The next thing you do is to construct a correlation
matrix of :-
S(i)T(j-k) where s(i) is stock i and j-k represents a time difference.
You then invert thir resultant matrix.
You have a maximum time difference of j-k and you can condition this
data by taking the first ten time differences and then combining times
for longer time differences. This is quite computationally intensive,
but it is basically what pure numerical stock prediction is all about.
- Ian Parker |
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| Alea... |
| Posted: Mon Sep 28, 2009 7:49 pm |
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Guest
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Hello,
thank you both for your replies.
at (no spam) Greg
I understand that the right choise of the input is most important.
But if i choose the input with the most information gain (WEKA is a
good tool for that) the result of the NN-run is pretty trivial:
Always bet on the favourite.
at (no spam) Ian P.
Thank you for the insight of computerized stock picking.
But if that strategy is initially successfull and known to the public,
it will have an impact on the stock price. This will tremendously
reduce the advantage.
Alea |
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| Tomasso... |
| Posted: Wed Sep 30, 2009 2:56 am |
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Guest
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post at (no spam) couptreffer.de wrote:
Quote: Hello,
NN is a fine tool for extracting various pattern. Of course that
requires the existence of a pattern. But what to do if patterns are
rarely found or if there is a very blurred pattern structure.
I think this applies to data whith a considerably random-like origin
(for example: stock markets, results of sportevents).
In this context: Wich kind of neural Network (MLP, RBF, ...) should
be used and what should be avoided?
Alea
Patterns in mostly random data. Interesting problem.
There is plenty of literature on noise reduction and pattern recognition.
The nub of it is that you have to know the kinds of patterns your looking
for. This is a subjective choice.
Ignore that fundamental requirement, and you'll find that everything is
a pattern.
Reference: Satosi Watanabe's Theorem of the Ugly Duckling.
Tom. |
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