How can i forecast future production using trained time series data?

how can i forecast future value for the data i trained using time series neural network tool? this is the script i got.

% Solve an Autoregression Time-Series Problem with a NAR Neural Network % Script generated by Neural Time Series app % Created 04-Apr-2018 20:22:20 % % This script assumes this variable is defined: % % RealData - feedback time series.

T = tonndata(RealData,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 feedbackDelays = 1:2; hiddenLayerSize = 10; net = narnet(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,{},{},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)

% Closed Loop Network % Use this network to do multi-step prediction. % The function CLOSELOOP replaces the feedback input with a direct % connection from the outout layer. netc = closeloop(net); netc.name = [net.name ' - Closed Loop']; view(netc) [xc,xic,aic,tc] = preparets(netc,{},{},T); yc = netc(xc,xic,aic); closedLoopPerformance = perform(net,tc,yc)

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

(e.g. i want to forecast for the production of (object) for 2030)

[Greg Heath]

PLEASE FORMAT

THANKS

GREG

Answer 1

[Greg Heath]

 1. Format your code so that it will run when copied and pasted.
 2. Test your code on the documentation example.
 3. If step 2 is satisfactory, try with your data.
 4. If step 2 is not satisfactory, write back with details.

Hope this helps

Greg

[Hadjie Esteves]

how can i format the code so that it will show the whole code here? thanks for responding is my question