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## TDNN multistep prediction with unknown future data for the target

Asked by Hugo Mendonça

### Hugo Mendonça (view profile)

on 16 Nov 2015
Latest activity Commented on by Hugo Mendonça

### Hugo Mendonça (view profile)

on 17 Nov 2015
Accepted Answer by Greg Heath

### Greg Heath (view profile)

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:
Xnew1(1:50);
Y1 = net(Xnew1,Xi,Ai);
Ans further, for a new data:
Xnew2(51:100);
Y2 = net(Xnew2,Xi,Ai);
Would it be the same? With the same Xi and Ai?
Thanks for helping!

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## 1 Answer

Greg Heath

### Greg Heath (view profile)

님의 답변 17 Nov 2015
채택된 답변

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
Greg
PS: See my tutorials

Hugo Mendonça

### Hugo Mendonça (view profile)

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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