Odd data preparation with NARX network
Hi, I am currently working on a NARX network for a time-series prediction problem. I am using a 3*4644 array as inputs and a 1*4644 array as my targets. I do not have any delay on the input and the feedback and here is the code that I am running: % Solve an Autoregression Problem with External Input with a NARX Neural Network % Script generated by Neural Time Series app % Created 16-Jul-2015 12:18:44 % % This script assumes these variables are defined: % % inputs - input time series. % targets - feedback time series. X = tonndata(inputs,true,false); T = tonndata(targets,true,false); % Choose a Training Function % For a list of all training functions type: help nntrain % 'trainlm' is usually fastest. % 'trainbr' takes longer but may be better for challenging problems. % 'trainscg' uses less memory. Suitable in low memory situations. trainFcn = 'trainlm'; % Levenberg-Marquardt backpropagation. % Create a Nonlinear Autoregressive Network with External Input inputDelays = 1:1; feedbackDelays = 1:1; hiddenLayerSize = 8; net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn); % Choose Input and Feedback Pre/Post-Processing Functions % Settings for feedback input are automatically applied to feedback output % For a list of all processing functions type: help nnprocess % Customize input parameters at: net.inputs{i}.processParam % Customize output parameters at: net.outputs{i}.processParam net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'}; net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'}; % 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,X,{},T); % Setup Division of Data for Training, Validation, Testing % For a list of all data division functions type: help nndivide net.divideFcn = 'divideblock'; net.divideMode = 'time'; % Divide up every sample net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100; net.divideParam.testRatio = 15/100; % Choose a Performance Function % For a list of all performance functions type: help nnperformance net.performFcn = 'mse'; % Mean Squared Error % Choose Plot Functions % For a list of all plot functions type: help nnplot net.plotFcns = {'plotperform','plottrainstate', 'ploterrhist', ... 'plotregression', 'plotresponse', 'ploterrcorr', 'plotinerrcorr'}; % 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) % Recalculate Training, Validation and Test Performance trainTargets = gmultiply(t,tr.trainMask); valTargets = gmultiply(t,tr.valMask); testTargets = gmultiply(t,tr.testMask); trainPerformance = perform(net,trainTargets,y) valPerformance = perform(net,valTargets,y) testPerformance = perform(net,testTargets,y) % View the Network %view(net) Now my problem is that whenever I take a look at the variables input, x, target and t, they don't look like what I was expecting to see. Basically my input and target variables look like [v1,v2,v3], [v2,v3,v4],[v3,v4,v5],... and v4,v5,v6,... respectively. Because of how I set up my network, I expected my x and t variables to look like [inputs at time t]| [v2,v3,v4], [v3,v4,v5], ... target from t-1 | v4 , v5 , ... and v5, v6, ... respectively (which clearly isn't perfect as I already have my target fedback from time t-1 in my input at time t but I know how to fix that) but instead I get something even weirder and this time unexpected: [v2,v3,v4], [v3,v4,v5], ... v5 , v6 , ... and v5, v6, ... as if the feedback from time t-1 and the target at time t were the same. So I don't know if it's normal or if I'm doing something wrong (possibly the delays that may need being set to zero). I'd be suprised if anyone understood what I mean but I'm afraid I don't know a better way to explain my problem. Sorry for the long and poorly structured post and thank you for your help !
Prashant Kumar answered .
2025-11-20
close all, clear all, clc
[ X0 T ] = simplenarx_dataset;
x0 = cell2mat( X0 );
X = con2seq( [ x0; x0; x0 ] );
x = cell2mat(X);
whos
% Name Size Bytes Class
% T 1x100 12000 cell
% X 1x100 13600 cell
% X0 1x100 12000 cell
% x 3x100 2400 double
% x0 1x100 800 double
ID = 1, FD = 1, H = 8
neto = narxnet( ID, FD, H );
[ Xo, Xoi, Aoi, To ] = preparets( neto, X, {}, T );
to = cell2mat(To); MSE00o = var(to,1); % 0.10199
net.divideFcn = 'divideblock';
rng(415941)
[ neto, tro, Yo, Eo, Xof Aof ] = train( neto, Xo, To, Xoi, Aoi );
% [ Yo Xof Aof ] = neto( Xo, Xoi, Ai); Eo = gsubtract(To,Yo);
NMSEo = mse(Eo)/MSE00o % 0.15076
NMSEtrno = tro.best_perf/MSE00o % 0.12489
NMSEvalo = tro.best_vperf/MSE00o % 0.18163
NMSEtsto = tro.best_tperf/MSE00o % 0.23894
% Check: 0.7*0.12489+0.15*(0.18163+0.23894) = 0.15051