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TDNN multistep prediction with unknown future data for the target

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Hi, everyone!
I am a little bit confused how to use a time delay neural network for multistep ahead prediction.
By my system characteristics, I must use a time delay neural network and not others. So, from previous measurement, I know the input and target time series, where:
Xdata(1:500) (input)
Tdata(1:500) (target)
Let's create the neural network:
net = timedelaynet(1:10,10);
[Xs,Xi,Ai,Ts] = preparets(net,Xdata,Tdata);
net = train(net,Xs,Ts,Xi,Ai);
I can clearly understand all the procedure until this point. However, I do not know how to prepare the new input data to be predicted and to use the net. I mean, in the future, I will just know the input data. So, how I could predict it? For example:
Y1 = net(Xnew1,Xi,Ai);
Ans further, for a new data:
Y2 = net(Xnew2,Xi,Ai);
Would it be the same? With the same Xi and Ai?
Thanks for helping!

Accepted Answer

Greg Heath
Greg Heath on 17 Nov 2015
In general, PREPARETS will yield the correct inputs. However, for TIMEDELAYNET, just use common sense:
Ai = {} % There is no feedback
Xi = Xnew2(:,1:10);
Xnew2s = Xnew2(:, 10:end);
Hope this helps.
Thank you for formally accepting my answer
PS: See my tutorials
  1 Comment
Hugo Mendonça
Hugo Mendonça on 17 Nov 2015
Thank you, Greg!
As I have seen, you are always helpful!
BTW, very god tutorial about NARNET. Maybe, in a very close future, I might use NARNET.
Thank you again!

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