title: How to plot the NAR predicted values in matlab
body: <p>After several reading up, I realise for my project I will need to use NAR because I am using history values to predict. I have a monthly data previous months. &amp; I need to predict for the next 12 months.</p>
  
  <pre><code>T = tonndata(Target_Set1,false,false);
  % Create a Nonlinear Autoregressive Network
  feedbackDelays = 1:2;
  hiddenLayerSize = 2;
  net = narnet(feedbackDelays,hiddenLayerSize);
  % Prepare the Data for Training and Simulation
  % The function PREPARETS prepares timeseries data for a particular network,
  % shifting time by the minimum amount to fill input states and layer states.
  % Using PREPARETS allows you to keep your original time series data unchanged, while
  % easily customizing it for networks with differing numbers of delays, with
  % open loop or closed loop feedback modes.
  [x,xi,ai,t] = preparets(net,{},{},T);
  % Setup Division of Data for Training, Validation, Testing
  net.divideParam.trainRatio = 70/100;
  net.divideParam.valRatio = 15/100;
  net.divideParam.testRatio = 15/100;
  % Train the Network
  [net,tr] = train(net,x,t,xi,ai);
  % Test the Network
  y = net(x,xi,ai);
  e = gsubtract(t,y);
  performance = perform(net,t,y)
  % View the Network
  view(net)
  % Plots
  % Uncomment these lines to enable various plots.
  %figure, plotperform(tr)
  %figure, plottrainstate(tr)
  %figure, plotresponse(t,y)
  %figure, ploterrcorr(e)
  %figure, plotinerrcorr(x,e)
  % Closed Loop Network
  % Use this network to do multi-step prediction.
  % The function CLOSELOOP replaces the feedback input with a direct
  % connection from the outout layer.
  netc = closeloop(net);
  [xc,xic,aic,tc] = preparets(netc,{},{},T);
  yc = netc(xc,xic,aic);
  perfc = perform(net,tc,yc)
  % Step-Ahead Prediction Network
  % For some applications it helps to get the prediction a timestep early.
  % The original network returns predicted y(t+1) at the same time it is given y(t+1).
  % For some applications such as decision making, it would help to have predicted
  % y(t+1) once y(t) is available, but before the actual y(t+1) occurs.
  % The network can be made to return its output a timestep early by removing one delay
  % so that its minimal tap delay is now 0 instead of 1.  The new network returns the
  % same outputs as the original network, but outputs are shifted left one timestep.
  nets = removedelay(net);
  [xs,xis,ais,ts] = preparets(nets,{},{},T);
  ys = nets(xs,xis,ais);
  stepAheadPerformance = perform(net,ts,ys)
  </code></pre>
  
  <p>How do I plot the predicted values after training and validation.From the examples I have seen for NARXNET normally they will use the code below.</p>
  
  <blockquote>
    <p>plot([cell2mat(T),nan(1,N);   nan(1,length(T)),cell2mat(yPred);<br>
    nan(1,length(T)),cell2mat(targetSeriesVal)]') legend('Original
    Targets','Network Predictions','Expected Outputs');</p>
  </blockquote>
  
  <p>Thanks in advance.</p>