Interruption in the plotted data

Question

Lilya 2015-12-5

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评论： Lilya 2015-12-8

predicted.fig

Hi all,

I use a neural network toolbox to predict the values of the last week in the used data year. When I plot the figure, the Interruption in the plotted data are appearing! Knowing that, the data are interpolated and there is no NaN in it.

SEE the attached figure

Please anyone can help I will appreciate it. Thank you

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Answer 1

Walter Roberson 2015-12-5

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编辑：Walter Roberson 2015-12-5

在 MATLAB Online 中打开

I loaded the figure and examined the data, and there certainly are NaN in it.

   h = openfig('predicted.fig');
    L = findobj(h, 'type', 'line');
    L7y = get(L(7), 'YData');
    find(~isnan(L7y(1:8594))
ans =
          8594

In other words, the first 8593 Y values of Expected Outputs are NaN and position 8594 is the first non-NaN. The same holds true for L(8), Network Predictions. The data for L(9), Original Targets, on the other hand, is valid only up to point 8593, and is NaN from 8594 onwards.

With the last non-NAN point from the left being 8593 and the first non-NaN point from the right being 8594, you will have a gap between 8593 and 8594, exactly like you are seeing.

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Lilya 2015-12-5

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In the original data there are no NaN values, it's only appeared after plotting. Here is the part of code which contain the problem

 inputSeriesVal  = X(end-N+1:end);
 targetSeriesVal = T(end-N+1:end); 
 %%3. Network Architecture
 delay = 3;
 neuronsHiddenLayer = 30;
 % Create a Nonlinear Autoregressive Network with External Input
 % net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
 net = narxnet(1:delay,1:delay,neuronsHiddenLayer);
 %%4. Training the network
 [Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries); 
 net           = train(net,Xs,Ts,Xi,Ai);
 view(net)
 [Y,xf,af] = net(Xs,Xi,Ai);
 % Performance for the series-parallel implementation, only 
 % one-step-ahead prediction
 perf          = perform(net,Ts,Y);
 %%5. Multi-step ahead prediction
 [netc,xi,ai] = closeloop(net,xf,af);
 [yPred,xf,af] = netc(inputSeriesVal,xi,ai);
 view(netc)
 figure;
 plot([cell2mat(targetSeries),nan(1,N);
      nan(1,length(targetSeries)),cell2mat(yPred);
      nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]')
 legend('Original Targets','Network Predictions','Expected Outputs')

Walter Roberson 2015-12-5

在 MATLAB Online 中打开

You can get it to draw the line connections, but they will not be valid line connections unless you ask it to predict the value at length(targetSeries)

Suppose I gave you data for points 1, 2, 3, and 4, and asked you to predict points 5 and 6, and then I drew a line through points 1 through 4 (the known) and I drew a line through points 5 and 6 (what you predicted) and then I complained that there is no line between points 4 and points 5. But I never asked you to predict anything between 4 and 5, so there is no data for that line to work with. I would have to give you points 1, 2, 3, and 4 and ask you to predict points 4, 5, and 6 in order to validly draw a line from 1 through 4 and a line from 4 through 6. (Of course if your prediction of those known values is not the same as the actual values, there may be a visual gap anyhow...)

You will find it easier if you change the way you are plotting.

ntarg = length(targetSeries);
plot(1:ntarg, mat2cell(targetSeries), ...
     ntarg : length(X), cell2mat(yPred_2), ...
     ntarg : length(X), cell2mat(targetSeriesVal_2) );

Here yPred_2 is like your current yPred except that it would have to be formed starting from x(ntarg) instead of from x(ntarg+1) like it is at present (that is, you need to ask to predict an existing value.) Likewise targetSeriesVal_2 would be starting from T(ntarg) instead of from T(ntarg+1) -- one value overlapping.

Walter Roberson 2015-12-6

在 MATLAB Online 中打开

Like this: notice the change to start the prediction data from exactly 1 point earlier than you had before

 inputSeriesVal  = X(end-N+0:end);     %CHANGED
 targetSeriesVal = T(end-N+0:end);     %CHANGED
 %%3. Network Architecture
 delay = 3;
 neuronsHiddenLayer = 30;
 % Create a Nonlinear Autoregressive Network with External Input
 % net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
 net = narxnet(1:delay,1:delay,neuronsHiddenLayer);
 %%4. Training the network
 [Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries); 
 net           = train(net,Xs,Ts,Xi,Ai);
 view(net)
 [Y,xf,af] = net(Xs,Xi,Ai);
 % Performance for the series-parallel implementation, only 
 % one-step-ahead prediction
 perf          = perform(net,Ts,Y);
 %%5. Multi-step ahead prediction
 [netc,xi,ai] = closeloop(net,xf,af);
 [yPred,xf,af] = netc(inputSeriesVal,xi,ai);
 view(netc)
 figure;
 ntarg = length(targetSeries);
 plot(1:ntarg, mat2cell(targetSeries), ...
      ntarg : length(X), cell2mat(yPred), ...
      ntarg : length(X), cell2mat(targetSeriesVal) );
 legend('Original Targets','Network Predictions','Expected Outputs')