Narx time series error

So below I am working with a narx time series model. I am working with purchase orders and am using the time series model to help predict quantity outputs for a given product ID for a specific city in relation to time. The for loop I have below allows me to sort through the data to select the portion of data that relates to the combination of one of the 16 different product ids with one of the 49 cities. When I run the time series model it explains and error of not enough input arguments. please help

y = [1 2 3 4 23 24 25 26 53 54 55 56 75 76 77 78];

for (x = 1:49)

    for (i=1:length(y))

        index = (Data_mat(:,2) == y(i) & Data_mat(:,3) == x);

        Data = Data_mat(index,[1 4])'

X = mat2cell(Data(1,:));

T = mat2cell(Data(2,:));

% 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)

% 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,X,{},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,X,{},T); ys = nets(xs,xis,ais); stepAheadPerformance = perform(nets,ts,ys)

    end
end