How do you make predictions with a trained Neural Network (NAR)?
Hello. I’m new here and I have a problem with the neural network toolbox that is unbearably frustrating because it should be the easiest thing in the world but I’m not getting it. My general Matlab skills are very moderate and I have never used the Neural Network function before until now. I want to create a neural network that based on an input data series can predict values in the future. From what I understand the Nonlinear Autoregressive neural network should be perfect for this and I have tried for hours and hours to watch all of Matlabs own tutorials on how to use the neural network toolbox and read about it but it seems like all the tutorials basically stop after the data has been trained and refuses to tell you how to make any useful predictions at all with the network. Or I am just simply missing something that is really obvious to everyone else except me. I read on a Matlab tutorial that they recommend you to use the GUI way of making a neural network first until you get the hang of it and can code it. So here is pretty much what I do when I follow the guides: Write nnstart -> go to Time series app -> select NAR and chose my 1x1826 data series as a matrix row (imported from an excel sheet) -> select 10 hidden neurons & 2 delays. Then I train the data using Levenberg-Marquardt and after the training it seems to have worked great. I click next and save the results and Finnish. My workspace is now full of things generated by the neural network, for example a data series called “output” which of course sounds promising, but it is 1x1824 cells, in other words 2 values shorter than the original data series (because delay was set to 2 I guess). And at this point I am totally lost. How do I make any predictions with this? I have also tried using the example input data in the toolbox instead of my own data series but it makes no difference. So after the training and everything is done, how in god’s name do I get the network to tell me the 1825th value of my data series?! I have tired generating the script and trying to understand it from there, and If I’m lucky the script can tell me things like “perfc = 1.5278” and “stepAheadPerformance = 5.8328e-04”. I’m not sure what that means but it feels like Matlab is bragging about how good it could theoretically predict the next values, but it sure doesn’t like to give up its predicted values without a fight! :) So can anyone help me out here? How do I predict values after the network has been trained?
John Williams answered .
2025-11-20
lookfor autocorrelation
then use the help and doc commands on functions that are listed. For example
help nncorr doc nncorr
autocorrelation neural narnet nncorr greg
net = narnet(FD,H);
1. Using capitals to indicate cell variables. 2. Using nncorr to find a reasonable subset of feedback delays 3. Using a for loop to find a reasonable value for H (e.g., Hmin:dH:Hmax) 4. Not using the default 'dividerand' because it destroys the correlations between the output and feedback signals 5. For each of numH candidate values for H, training success depends on starting with a good set of random initial weights. The best way to find one or more is to have an inner for loop over Ntrial random weight initializations that are created by the configure function. 6. Explicitly initializing the random number generator before the outer loop so that you can duplicate any of the numH*Ntrials designs 7. Often closing the loop on an openloop design to obtain netc does not yield acceptable results when inputted with original data. Therefore, the closeloop net should be trained beginning with the weights obtained from the openloop design to obtain netc2.
% And I’m not sure what to do with the LHS syntax thing, I did have xs ts xi and ai in my workspace so I tried adding the piece of code you wrote: % % [ net tr Ys Es Xf Af ] = train( net, Xs, Ts, Xi, Ai );
This code replaces the 3 step script
[ net tr ] = train( net, Xs, Ts, Xi, Ai ); [ Ys Xf Af ] = net( Xs, Xi, Ai ); Es = gsubtract(net,Ts,Ys); Finally, to predict into the future M timesteps beyond the end of the target data Xic2 = Xf; Aic2 = Af; Ypred = netc2( cell(1,M), Xic2, Aic2);