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I don't want to be a party pooper, but if this thread goes on much longer,
I'm going to go back to building networks.
I don't need this!!!!!!!!!!!!!!!!!!!!!!!!!!
(or there again maybe I do I'm weakening already.)
DJ
----- Original Message -----
From: "rudolf stricker" <lists@xxxxxxxxxxx>
To: <metastock@xxxxxxxxxxxxx>
Sent: Friday, February 09, 2001 6:15 AM
Subject: Re: Artificial Intelligence, expert and neural
>
> Jeff and others,
>
> On Wed, 7 Feb 2001 10:12:29 -0500 (EST), you wrote:
>
> >You do make an important distinction about making "one's targets
> >clear" and give the example of predicting turning points. I
> >do know of some success with predicting turning points with NNs.
> >
> >I should have made the distinction that I don't know of any
> >NN-based models that have done much better at price forecasting
> >than you could obtain by a random walk model. I have
> >a bit of firsthand knowledge of this, I worked for a quant
> >firm at the CBOT in the late 80s... this is at a time when
> >NNs were all the rage... and continued that way into the 90s.
> >Folks were treating NNs as blackboxes that might instantly
> >give them an edge. The rage gradually faded...
>
> Any serious modeling task should have a clear target, but imo there is
> more to consider:
>
> NN is only a _modeling technique_ (one among many). Therefore, we
> first have to have an adequate _model setup_ (e.g. in terms of input /
> output parameters) for any problem to be modeled successfully.
>
> GA is only an _optimization technique_ (one among many). So it can be
> used to optimize e.g. the parameters of a neural model (like
> structure, size, transfer functions, convergency criteria, etc).
>
> Additionally, we have to turn our attention to the _generalization
> aspect_, because we want only to pick-up features and patterns from
> the past, that will also be valid for the future, at least to some
> extent. Of course, here again are some helpful procedures, like
> cross-validation, etc.
>
> And only, if we use a valid _model setup_, employing an appropriate
> _modeling technique_, carefully used in context with a powerful
> _optimization technique_, and all done under consequent consideration
> of _generalization aspects_, we possibly may be successful.
>
> Imo, as a consequence, it does not make much sense to stick to
> something like:" NNs are bad", or "GAs are powerful". It all depends
> from the context, where and how these tools & techniques are used.
>
> >My intent here is not to be overly-pessimistic or confrontational,
> >but rather I want to point out that with NNs in particular
> >(and I presume also with GAs) there is a lot of preprocessing
> >of data and understanding of the underlying phenomenon necessary
> >in order to get decent results.
>
> Imo, its not necessary _to really understand_ the underlying
> phenomenons, if we use "open-minded" modeling techniques, like e.g.
> NNs. It is sufficient to have a valid model set-up (i.e. parameter
> set) _and_ enough(!) valid examples from the past. Working this line,
> we even can "discover" new knowledge and increase our understanding.
>
> What imo could be much more important, is to do some basic tests on
> the modeling problem to solve, to get some impression, if it might be
> possible to be successful. The test procedures from nonlinear dynamic
> modeling theory can be _very_ helpful here, especially if we work on
> time series (e.g. prices). This way we can avoid to spend too much
> work on modeling problems, which can be a-priori identified as truly
> random, whereas other problems, which are said to be random often
> offer nice chances for appropriate modeling.
>
> mfg rudolf stricker
> | Disclaimer: The views of this user are strictly his own.
>
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