If you look at the significant lags of the output autocorrelation function and the output/input crosscorrelation function, you can deduce how far ahead you might be able to predict AND the number of delays that you might have to use to accomplish that goal. In general,
y( : , t : t + d ) = f( x( : , t - id : t) , y( : , t - ld : t - 1))
For a special type of NARXNET scalar output prediction
y( t + d ) = f( x( : , t - id : t) , y( t - ld : t - 1))
for d = 10, id >= 0, ld >= 1
Whereas for a special type of TIMEDELAYNET prediction
y( t + d ) = f( x( : , t - id : t) )
PS: timedelaynet(ID) may be internally different than narxnet(ID,[])!
Hope this helps.
Thank you for formally accepting my answer
Greg
