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How to predict future responses y(t + 1) from the training of a narxnet network with past data of x (t) and y (t)? (NARXNET)

Asked by Cesar Diaz on 12 Feb 2019
Latest activity Commented on by Cesar Diaz on 13 Feb 2019
Greetings,
I hope someone could help me with the next question:
As input variables for the NARXNET (time series) type neural network I have the following data (cells arrays):
INPUTS: x(t): 1 x 55, each column with 18 x 1. The 18 variables are entries.
OUTPUTS: y(t): 1 x 55, each column with 6 x 1. The 6 variables are outputs.
The 18 input variables are different from 6 output variables. There have high correlations between them. I have a model using a step-by-step regression (multiple) to compare this results with the final results of the NARXNET net. I'm using MSE as statistical error measurement between methods.
I wish to predict Y(1) only by using a input matrix x(t): 1 x 55 (each column 18 x 1 variables).
How could I evaluate the network (net) created using a matrix x(t):1 x 55 (each column 18 x 1 variables) different from the one used during training?
%-------------------------TRAINING RESULTS------------- (ANY COMMENTS ABOUT THIS RESULTS?)
RESULTADOS_NET1.jpg
I'm using the following code from the Matlab examples in ntstool.
This is the code (any correction and suggestion will be welcome):
%---------------------------------------------ANN FORECASTING--------------------------------------------
inputSeries = INPUTS; %x(t): 1 x 55, each column with 18 x 1. The 18 variables are entries.
targetSeries = OUTPUTS; %y(t): 1 x 55, each column with 6 x 1. The 6 variables are outputs.
%Bayesian model
trainFcn = 'trainbr';
% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:0;
feedbackDelays = 1:1;
hiddenLayerSize = 30;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);
%view(net)
[inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);
% Set up Division of Data for Training, Validation, Testing
net.divideFcn = 'divideblock';
net.divideMode = 'value';
net.trainParam.epochs = 10000;
net.trainParam.goal = 0;
net.trainParam.lr = 0.001;
net.divideParam.trainRatio = 85/100;
net.divideParam.valRatio = 15/100;
net.divideParam.testRatio = 15/100;
%MSE
net.performFcn = 'mse';
% Train the Network
[net,tr] = train(net,inputs,targets,inputStates,layerStates);
% Test the Network
outputs = net(inputs,inputStates,layerStates);
errors = gsubtract(targets,outputs);
performance = perform(net,targets,outputs)
% View the Network
view(net)
% Plots
% Uncomment these lines to enable various plots.
% figure, plotperform(tr)
% figure, plottrainstate(tr)
% figure, plotregression(targets,outputs)
% figure, plotresponse(targets,outputs)
% figure, ploterrcorr(errors)
% figure, plotinerrcorr(inputs,errors)
% Closed Loop Network
% Use this network to do multi-step prediction.
% The function CLOSELOOP replaces the feedback input with a direct
% connection from the output layer.
netc = closeloop(net);
netc.name = [net.name ' - Closed Loop'];
view(netc)
[xc,xic,aic,tc] = preparets(netc,inputSeries,{},targetSeries);
yc = netc(xc,xic,aic);
closedLoopPerformance = perform(netc,tc,yc)
% Early 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,1);
nets=net;
nets.name = [net.name ' - Predict One Step Ahead'];
view(nets)
[xs,xis,ais,ts] = preparets(nets,inputSeries,{},targetSeries);
ys = nets(xs,xis,ais);
earlyPredictPerformance = perform(nets,ts,ys)

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1 Answer

Answer by Greg Heath
on 13 Feb 2019
 Accepted Answer

YOU DO NOT HAVE X and Y !!!
YOU HAVE X and T where
T = Ydesired
Hope this helps
Thank you for formally accepting my answer
Greg

  1 Comment

Hi Greg, thanks for the answer.
But, how I could send a input matrix x(t), different from the training matrix?. Over the last code, do you could help me with that?.
So, the forecasted responses are:
yc = netc(xc,xic,aic); %CLOSED LOOP
ys = nets(xs,xis,ais); %ONE STEP AHEAD
How I could make a new forecast from:
outputs = net(inputs,inputStates,layerStates); %OPENED LOOP
I need to load a new x(t) matrix for make a forecast T(t).
Thanks Greg.

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