What are the INPUTS , TARGETS, and OUTPUTS?

I am getting confusing about Inputs data set, outputs and target. I am studying about Artificial Neural Network in Matlab, my purposed is that I wanted to use the history data (I have rainfall and water levels for 20 years ago) to predict water level in the future (for example 2014). So, where is my inputs, targets, and output? For example i have a Excel sheet data as [Column1-Date| Column2-Rainfall | Column3 |Water level]

I am using this code to prediction, but it could not predict in the future, can anyone help me to fix it again? Thank you .

    %%1. Importing data
    Data_Inputs=xlsread('demo.xls'); % Import file

    Training_Set=Data_Inputs(1:end,2);%specific training set

    Target_Set=Data_Inputs(1:end,3); %specific target set

    Input=Training_Set'; %Convert to row

    Target=Target_Set'; %Convert to row

    X = con2seq(Input); %Convert to cell

    T = con2seq(Target); %Convert to cell

    %%2. Data preparation

    N = 365; % Multi-step ahead prediction

    % Input and target series are divided in two groups of data:
    % 1st group: used to train the network

    inputSeries  = X(1:end-N);

    targetSeries = T(1:end-N);

    inputSeriesVal  = X(end-N+1:end);

    targetSeriesVal = T(end-N+1:end); 
    % Create a Nonlinear Autoregressive Network with External Input
    delay = 2;
    inputDelays = 1:2;
    feedbackDelays = 1:2;
    hiddenLayerSize = 10;
    net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);

    % 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.

    [inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries);

    % 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,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 outout 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);
    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)

    %%5. Multi-step ahead prediction

    inputSeriesPred  = [inputSeries(end-delay+1:end),inputSeriesVal];

    targetSeriesPred = [targetSeries(end-delay+1:end), con2seq(nan(1,N))];

    [Xs,Xi,Ai,Ts] = preparets(netc,inputSeriesPred,{},targetSeriesPred);

    yPred = netc(Xs,Xi,Ai);

    perf = perform(net,yPred,targetSeriesVal);

    figure;

    plot([cell2mat(targetSeries),nan(1,N);
      nan(1,length(targetSeries)),cell2mat(yPred);
      nan(1,length(targetSeries)),cell2mat(targetSeriesVal)]')

    legend('Original Targets','Network Predictions','Expected Outputs');

[Greg Heath]

The best way to obtain help is to apply your code to one or more of the NNTBX examples so that we can compare results.

help nndatasets