Train closed-loop narnet using composite array?

Dear all,

I am using neural network time-series NARNET for predicting the future value. To do so, I need to train the closed-loop neural network. Due to RAM limitations, system requires using composite array.

Data series (having mean = 0 and var =1) looks like below:

Can Someone please suggest how to use composite array for closed-loop NARNET network. Below is my code and data:

    T=tonndata(data(1:end,1),false,false);%convert the data into neural input format
    delay=[1:481]; %value of delay found using autocorrelation
    hiddenlayersize=6; %found using hit and trial (optimum for our application)
    trainFcn='trainlm';
    net=narnet(delay,hiddenlayersize,'open',trainFcn);
    net.trainParam.epochs = 1000;
    net.divideFcn = 'dividerand';  % Divide data randomly
    net.divideMode = 'time';  % Divide up every sample
    net.divideParam.trainRatio = 96/100;
    net.divideParam.valRatio = 2/100;
    net.divideParam.testRatio = 2/100;
    net.trainParam.min_grad=1e-7;
    net.trainParam.max_fail=500;
    net.performFcn = 'mse'; 
    net.performParam.normalization = 'standard';
    net.input.processFcns={'removeconstantrows','mapminmax'}
    [Xso,Xio,Aio,Tso]=preparets(net,{},{},T);
    rng('default')
    net=train(net,Xso,Tso,Xio,Aio,'useParallel','yes');
    [Yso,Xfo,Afo]=net(Xso,Xio,Aio);
    [netc,Xic,Aic]=closeloop(net,Xfo,Afo);
    [Xc,Xic,Aic,Tc] = preparets(netc,{},{},T);
    rng('default')
  xc = Composite;
  tc = Composite;
  xic=composite;
  aic=composite;
  xc{1} = Xc(0:500);
  xc{2} = Xc(500:773);
  tc{1} = Tc(0:500);
  tc{2} = Tc(500:773);
  xic=Xic(1:end);
  aic=Aic(1:end);

netc=train(netc,xc,tc,xic,aic);

Since netc is NARNET closed loop, so size(Xc) is 0*773. Due to this I am not able to define xc as composite.