Code_NARXnet.m

% Solve an Autoregression Problem with External Input with a NARX Neural Network
% Script generated by Neural Time Series app
%
% This script assumes these variables are defined:
%
%   NN-IN   - input time series.
%   NN-TARG - feedback time series.

clear; clc; format long; 

IN = readmatrix('NN-IN.xlsx');
TARG = readmatrix('NN-TARG.csv');

X = tonndata(IN,false,false);
T = tonndata(TARG,false,false);

% Choose a Training Function
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainscg';  % Scaled conjugate gradient backpropagation.

% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:2;
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,'open',trainFcn);

% Prepare the Data for Training and Simulation
[x,xi,ai,t] = preparets(net,X,{},T);

% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 70/100;
net.divideParam.valRatio  = 15/100;
net.divideParam.testRatio = 15/100;

tic

% Train the Network
[net,tr] = train(net,x,t,xi,ai);

toc

% Test the Network
y = net(x,xi,ai);
e = gsubtract(t,y);
performance = perform(net,t,y)

% View the Network
view(net)

% Plots
figure,  plotperform(tr)
%figure, plottrainstate(tr)
figure,  ploterrhist(e)
%figure, plotregression(t,y)
%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 output layer.
netc = closeloop(net);
netc.name = [net.name ' - Closed Loop'];
%view(netc)
[xc,xic,aic,tc] = preparets(netc,X,{},T);
yc = netc(xc,xic,aic);
closedLoopPerformance = perform(net,tc,yc);

% Step-Ahead Prediction Network
nets = removedelay(net);
nets.name = [net.name ' - Predict One Step Ahead'];
%view(nets)
[xs,xis,ais,ts] = preparets(nets,X,{},T);
ys = nets(xs,xis,ais);
stepAheadPerformance = perform(nets,ts,ys);