How to plot the NAR predicted values in matlab?

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Hi there. 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. & I need to predict for the next 12 months.
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)
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.
plot([cell2mat(T),nan(1,N);
nan(1,length(T)),cell2mat(yPred);
nan(1,length(T)),cell2mat(targetSeriesVal)]')
legend('Original Targets','Network Predictions','Expected Outputs');
Thanks in advance.

采纳的回答

Greg Heath
Greg Heath 2014-1-6
编辑:Greg Heath 2014-1-6
1. Use the Target autocorrelation function to determine a good set of feedback delays. Search the NEWSGROUP and ANSWERS using
greg nncorr
2. Find a good value for H, the number of hidden nodes, by using a double loop over candidate values and initial weights. Search N & A:
greg narnet Ntrials
greg narxnet Ntrials
3. Plot y and yc. If perfc is much worse than performance, continue training with
[ netc trc yc ec xcf acf ] = train(netc, x, t, xi, ai);
4. Search N & A:
greg closeloop
Hope this helps.
Greg
  4 个评论
Madhav Kathal
Madhav Kathal 2016-2-22
hi greg! i have gone through some of your answers and you consistently say that we can find feedback delays by plotting the autocorrelation function. i am currently working on narnet and i am unable to find the optimum no. of delays i should use. How exactly do i find it? Steps or any links would help. i have gone through the NEWSGROUP and ANSWERS. Thanks !
Greg Heath
Greg Heath 2016-2-22
In general, it is too hard to find the optimal set of hidden nodes and delays. I am just happy to find the minimum number of delays and hidden nodes that will model 95% of the average target variance.
I have many postings dealing with that search NEWSGROUP and ANSWERS using
narnet nncorr
Write back in a NEW POST if you have problems understanding or using it.
Hope this helps
Greg

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