Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!purdue!decwrl!nsc!voder!berlioz!andrew
From: andrew@berlioz (Andrew Palfreyman)
Newsgroups: comp.ai.neural-nets
Subject: Re: How to simulate Foreign Exchange Rates
Summary: association...
Message-ID: <129@bach.nsc.com>
Date: 26 Apr 89 09:06:30 GMT
References: <10198@orstcs.CS.ORST.EDU>
Organization: National Semiconductor, Santa Clara
Lines: 25

In article <10198@orstcs.CS.ORST.EDU>, harish@mist.CS.ORST.EDU (Harish Pillay) writes:
> I am taking a grad course on NN and am planning on doing a project trying to
> predict foreign exchange rates specifically the following:
>    US$ vs British Pound vs Japanese Yen vs Singapore $ vs German Marks
> I am using NeuralWorks and am thinking of using the backprop strategy.
> ...but my problem is in trying to train the network.  Has anyone out there
> done anything similar to this?  If so, what desired output values did
> you use to train?  I understand that it is naive to just take the rates
> themselves and try to get a pattern or correlation.  Should I be looking
> at other values too?  What kind of transfer function should I use?  I think
> one hidden layer may be sufficient.

One brute force method, to separate the chicken from the egg, might be to
use the changes instead of the absolute values (especially since you're
using localised data which doesn't span a boom or a crash).
Maybe then you could use 3 inputs in parallel (3 currencies) and 2 outputs,
and just ring the changes (5c3 = 10 ways) until the input deltas produce
correct output deltas. An associative net might do this better.
Else, you could play with recursive nets (Jordan, etc.), whereby you
try and predict tomorrow's 5-vector, given today's.
-- 
Andrew Palfreyman 		USENET: ...{this biomass}!nsc!logic!andrew
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