Good day I would like to get a clear code of how to use NARX

Good day,

I would like to get a clear code of how to use NARX tool in Matlab to make prediction.

for example, If I have a pressure vector of 93 data points, and I trained the network for this data vs time for the first 70 data, how can I predict the remaining 23 data using the NARX I built? Also, is the prediction I made for future?

see my code below and let me know if this is the correct way to do it

clear;

load Pressure

X=time(1:70);

T=Pressure(1:70);

X_2=time(71:end);

T_2=Pressure(71:end);

X_2 = tonndata(X_2,false,false);

T_2 = tonndata(T_2,false,false);

% Solve an Autoregression Problem with External Input with a NARX Neural Network

% Script generated by Neural Time Series app

% Created 15-Feb-2017 12:55:53

% This script assumes these variables are defined:

% X - input time series.

% T - feedback time series.

X = tonndata(X,false,false);

T = tonndata(T,false,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 = 1:2;

feedbackDelays = 1:2;

hiddenLayerSize = 10;

net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize,\'open\',trainFcn);

% 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

net.divideParam.trainRatio = 70/100;

net.divideParam.valRatio = 15/100;

net.divideParam.testRatio = 15/100;

% 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)

% View the Network

view(net)

% Plots

% Uncomment these lines to enable various 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)

nets = removedelay(net);

[xs_f,xis_f,ais_f,ts_f] = preparets(nets,X_2,{},T_2);

ys_f = nets(xs_f,xis_f,ais_f);

plot(time,Pressure);

hold on

plot(time(71:end-1),cell2mat(ys_f),\'g\')