tha meaning of delay in neural net time series

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Hi All,
I want to be sure about the delay in time series meaning For example, when the I write the input delay is 2, and the feedback delay is 3 does that mean its change from 1:2, 1:3? And the other question is can I take the delay in NAREXNET as an intervals? (i.e. 1:2:4)?
Thank you

Accepted Answer

Greg Heath
Greg Heath on 18 Dec 2015
Edited: Greg Heath on 4 Jan 2016
net = narxnet(ID,FD,H)
ID is a row vector of NONNEGATIVE, INCREASING BUT NOT
NECESSARILY CONSECUTIVE, INTEGERS
FD is a row vector of POSITIVE , INCREASING BUT NOT NECESSARILY
CONSECUTIVE, INTEGERS
ID and FD do not have to have the same length or integers
in common
ID = 2 IS NOT THE SAME AS ID = 1:2 = [ 1 2 ]
ID = [ 1:2:4 ] = [ 1 3 4 ]
If ID = [ 0:2:4 ] and FD = [ 1:3 ], then
y(t+4) = f( x(t+4), x(t+2), x(t), y(t+3, y(t+2), y(t+1) )
Hope this helps,
Thank you for formally accepting my answer
Greg
  2 Comments
Lilya
Lilya on 21 Dec 2015
Dr. Heath excuse me, but I want to be sure about the value of MSE00 I run the command MSE00 = mean(var(t',1)) to normalized the data the result is 196.8228 t [1 8761] and N= 8761 is it acceptable MSE00?
Greg Heath
Greg Heath on 4 Jan 2016
It has to be acceptable. It is just the average variance of your target variables.
The important point is that it is the minimum MSE that can be achieved with the NAIVE constant output model, that yields the same constant output regardless of input. (The next best model is a linear model)
With a little thought you can prove that if the output is constant, regardless of input, then the constant that minimizes MSE is just the mean of the target matrix and the minimum MSE is just the average target variance.
Therefore, it is a very appropriate reference for normalization.
Also note that if the target columns are standardized (zero-mean/unit-variance) via zscore or mapstd, then MSE00 = 1.
Hope this helps.
Greg

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More Answers (1)

Fatma HM
Fatma HM on 14 Jan 2019
Edited: Greg Heath on 14 Jan 2019
Hi All,
I want to know how I can find the size of the real and the size of the estimated in Neural networks when i have input, output and error ??
GREG:
Real and estimated what?

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