How to calculate correctly Ntrneq, Nw, Hub, Hmax, MSEgoal for MULTIPLE SEQUENCES in neural networks?

I am trying to find the minimum number of nodes in hidden layer that satisfies my MSEgoal.

I have my input and output cell arrays of {1x491} with [2x33] values. Technically, I have 33 time series of length 491 with padded NaNs with 2-inputs and 2-outputs.

If I do:

x  = cell2mat(X);
t  = cell2mat(T);

[ I N ] = size(x);           % [ 2 16203]  % Including the NaN values
[ O N ] = size(t);

N = the total number of point that will be use for training, including the NaNs.

However, I assume the I do not want to count the NaNs to calculate Ntrneq so I removed them:

[ I N ] = size(x(:,any(~isnan(x))));           % [ 2 12116] % Removed NaN values from the count
[ O N ] = size(t(:,any(~isnan(t))));

However, Ntreq is huge = 24202, with a HUB of 967 and Hmax of 96. Which is also huge compared to h=10 that have been using in a pilot test where error are in the order of 1/10000.

But if I do:

[ I N ] = size(X);           % [ 1 491] 
[ O N ] = size(T);

Am I only calculating Ntrneq, Nw, Hub, Hmax as if I only had 1 time series but still I&O =2?

This is my complete code:

x  = cell2mat(X);
t  = cell2mat(T);

%[ I N ] = size(x);           % [ 2 16203]  % Including the NaN values
% [ O N ] = size(t);

[ I N ] = size(x(:,any(~isnan(x))));           % [ 2 12116] % Removed NaN values from the count
[ O N ] = size(t(:,any(~isnan(t))));

% [ I N ] = size(X);           % [ 1 491] % Removed NaN values from the count
% [ O N ] = size(T);

Ntrn = N % 12116 use all data set only for finding h

rng('default')

FD   = 10:15; % Determine with pilot chracterization. It will be updated with auto-correlation of targets 
ID   = 10:15; % Determine with pilot chracterization. It will be updated with cross-correlation of targets and inputs
steps=15; % Lag same es MXFD or MXID = max(FD) or max(ID)

NFD  = length(FD); NID  = length(ID);
Ntrneq = (Ntrn-steps)*O %24202,  I deleted the lags
Hub     =  -1+ceil( (Ntrneq-O) / ((NID*I)+(NFD*O)+1)) %  967
Hmax    =  floor(Hub/10) % 96
Hmin    = 0; dH      = 1; Ntrials = 10; j=0;
rng(0)
for h = Hmin:dH:Hmax
    j = j+1;
    if h == 0
        net = narxnet( ID, FD, [] );
        Nw =  ( NID*I + NFD*O + 1)*O;
    else
        net = narxnet( ID, FD, h );
        Nw =  ( NID*I + NFD*O + 1)*h + ( h + 1)*O;
    end
    Ndof            = Ntrn-Nw;
    [ Xs Xi Ai Ts ] = preparets(net,X,{},T);
    ts              = cell2mat(Ts);
    xs              = cell2mat(Xs);
    MSE00s          = mean(nanvar(ts',1)); %I omitted the NaNs
    MSE00as         = mean(nanvar(ts',0));
    MSEgoal         = 0.01*Ndof*MSE00as/Ntrneq;
    MinGrad         = MSEgoal/10; % or MSEgoal/100;
    net.trainParam.goal      =  MSEgoal;
    net.trainParam.min_grad  =  MinGrad;
    net.divideFcn            =  'dividetrain';
    net.performParam.normalization = 'standard'; % Does this affect the performance because my MSEgoal is in the normal range.
    for i = 1:Ntrials
        net            =  configure(net,Xs,Ts);
        [ net tr Ys ]  =  train(net,Xs,Ts,Xi,Ai);
        ys             =  cell2mat(Ys);
        stopcrit{i,j}  = tr.stop;
        bestepoch(i,j) = tr.best_epoch;
        MSE            = mse(ts-ys);
        MSEa           = Ntrneq*MSE/Ndof;
        R2(i,j)        = 1-MSE/MSE00s; %? different ranges?
        R2a(i,j)       = 1-MSEa/MSE00as; %? different ranges?
end
stopcrit   =  stopcrit   
bestepoch  =  bestepoch
R2         =  R2
R2a        =  R2a
%Totaltime  =  toc

Lastly, in my code I am not normalizing my inputs and outputs but I used:

net.performParam.normalization = 'standard';

becasue my outputs have different ranges. Does that mean that my

      net.trainParam.goal      =  MSEgoal;
        net.trainParam.min_grad  =  MinGrad;

are on different ranges (data vs training). so my R2, too?

I have posted this question about using zscore on my data set:

https://www.mathworks.com/matlabcentral/answers/412328-how-to-standardize-normalize-values-multiple-sequences-in-neural-networks

because I couldn't make it work.

I have attached my X and T.

Thank you,