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

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Perez_ramos - 2021-07-28T11:59:53+00:00
Question: 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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    • Expert Answer

    Profile picture of Prashant Kumar Prashant Kumar answered . 2025-11-20

    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. Reusing the same data division biases the resulting test subset estimate.
     
    Quite often the unbiased constraint of this procedure is violated by including the test subset in the choice of the best design. If this is done, I recommend for the sake of caution, that another round with a new random division still be performed.
     
    The above procedure is difficult to implement with time series because uniform spacing should be maintained to preserve output-feedback autocorrelations and input-output cross-correlations.
     
    Now, I do not understand your problem because I do not understand why you are using a validation subset without a test set to estimate nondesign performance. 


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