Kostrzewa asked . 2021-08-04

Neural Network generated code gives me different result then my own code

function y_predykcja = matlabNeuralNetworkScript(training_set, horizon)
%T = simplenarTargets;
T= tonndata(training_set,true,false);
trainFcn = 'trainlm';  % Levenberg-Marquardt
feedbackDelays = 1:2;
hiddenLayerSize = 10;
net = narnet(feedbackDelays,hiddenLayerSize,'open',trainFcn);
net.input.processFcns = {'removeconstantrows','mapminmax'};
[x,xi,ai,t] = preparets(net,{},{},T);
net.divideParam.trainRatio = 0.78;
net.divideParam.valRatio = 0.22;
net.divideParam.testRatio = 0;
net.trainParam.showWindow = false;
net.performFcn = 'mse';  % Mean squared error
% Train the Network
[net tr Ys Es Xf Af ] = train(net,x,t,xi,ai,'useParallel','no');
y = net(x,xi,ai);
y_predykcja = zeros(1,horizon);
for i=1:horizon
  Xnew = net(x,Xf,Af);
  Xf = [Xf Xnew];
  Xf = Xf(1,2:3);
  y_predykcja(1,i) = cell2mat(Xf(1,2));
end
end

My solution is working... but not as good as normal generated by Matlab script. For example I use series load ice_dataset. If I use ntstool where the whole series is divied into ratio 0.55/0.15/0.3 I get MSE 0.02. When I split this data to training_set (0.7 of whole set) and then use my script I get MSE 2. If I use sinus seris MSE of Matlab script is 1.4-e10 in my case is 1.4-e8. Could anybody explain me why ? How to fix my script to get expected accuracy ?

neural network , nn , ntstool , prediction , forecasting , time , time series