MATLAB: By including a moving window of fixed length in the input vector of MLP, is the Back-propagation ANN equivalent to NAR model

Deep Learning Toolboxinput nodesneural networks

If this is the case, how we can add the moving window? Supposing that the lag is equal to 3, for example:
N= lenght(data);
d=timestep ahead;
input  = data( 1:N-d);   % No transpose; 
target = data( 1+d : N );
MSE00  = var(target',1)  % Reference MSE
net = fitnet;   % default H = 10 
net.divideParam.valRatio = 10/100;
net.divideParam.testRatio = 20/100;
[net tr output error ] = train(net, input, target);
%output = net(input); 
error = target - output;
NMSE = mse(error)/MSE00 % Range [ 0 1 ]
R2   = 1- NMSE
Thanks

Best Answer

1. When you insert code try to make sure it runs.
 N= lenght(data);  % ERROR

 d=timestep ahead; % ERROR
2. Replace TRAIN with ADAPT
Hope this helps.
Thank you for formally accepting my answer
Greg
Related Question