The much step forecast with usage of the recurrence neural networks

2013 4 1
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Greg Heath
Greg Heath 2013-4-1

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You have to fill in the details. The main theme is
help/doc narxnet
help/doc preparets
help/doc closeloop
search narxnet greg
Find significant delays from the autocorrelation of t and the cross-correlation of x and t. Create delay row vector inputs ID and FD
Find H by trial and error. First try values 0 <= H <= Hub
Hub = -1 + ceil( (Neq-O) / (I+O+1))
net = narxnet(ID,FD,H);
net.divide.Param = 'divideblock';
[ Xs Xi Ai Ts] = preparets( net, x, {},t );
[net tr Ys Es Xf Af ] = train(net,Xs,Ts,Xi,Ai);
netc = closeloop(net);
[ netc trc Ysc Esc Xfc Afc ] = train(netc, Xs,{},Xic Aic);
plot hold on plot(Ts) plot(Ysc,'r')
trc = trc
Hope this helps.
Greg

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Vadim
Vadim 2013-4-2

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NARNET works remarkably, but for the decision of my problem hybrid NARNET and the RECURRENT GRID is necessary. The forecast for 1 step forward is calculated excellently, but the multistep-by-step forecast does not work. I think the reason in wrong displacement because of delay lines. Believed that someone faced a similar problem, it would not be desirable to look at once in source codes and to understand there.

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Greg Heath
Greg Heath 2013-4-4
Can't help without seeing your code. Would help if you used it on one of the timeseries nndatasets so that we can compare results.
help nndatasets
Greg

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