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How could I do a Multi-step ahead Prediction without know the input serie validation?

 I am learning neural networks, I am doing some small exercises to learn it, but I have a huge question that I cannot figure it out. If I have a time series(input=X, target=T), and I am using input_training=X(1:end-N), target_train=T(1:end-N). My validation data is: input_val=X(end-N+1:end), target_val=T(end-N+1:end). I am testing a NARX, if this happen: - input_val(it is available). - target_val(it is not available). If that conditions happen I get good predictions(error<3%), but I would like to know how could I get good predictions if : - input_val(it is not available). - target_val(it is not available).



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What data division function are you using?
 
Training, Validation and Testing are three separate functions. In order to obtain unbiased estimates of performance on nondesign data:
 
Total = Design + Test
 
Design = Training + Validation
 
Training subset:
 
 Used to directly estimate unknown weight values ( e.g., via gradient descent)

Validation subset:

 Used REPETETIVELY with Training set to determine the best set of training 
parameters (e.g., No of hidden nodes, stopping epoch, selection of input and feedback delays, etc) and best of multiple random weight initialization designs.

Test subset:

 Used ONCE and ONLY ONCE on the best design w.r.t. validation subset 
 performance to obtain an UNBIASED estimate of performance on nondesign 
 data (AKA generalization).
If the test set estimate is unsatisfactory, the data set should be randomly divided again and the entire procedure duplicated.

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