Hello Everyone,
As a beginner i am trying to understand the use of neural networks in time series prediction. I am trying to develop a model which can predict a flood forecast, but i am not understanding what is use of Input and Target delays in the network and also how should i give multiple varibles as inputs as i have 4 input parameteres with me. but currently i am providing only two.
Below is code attached please let me know if it is correct
Data_Inputs=xlsread('Book1.xlsx'); % Import file
%The training data sample are randmonized by using the function'randperm'
Shuffling_Inputs=Data_Inputs(randperm(end),1:2); % integers (training sample)
Training_Set=Data_Inputs(1:end,1);%specific training set
Target_Set=Data_Inputs(1:end,2); %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
% Create a Nonlinear Autoregressive Network with External Input
inputDelays = 1:4;
feedbackDelays = 1:4;
hiddenLayerSize = 10;
net = narxnet(inputDelays,feedbackDelays,hiddenLayerSize);
net.trainParam.epochs=1000;
% Prepare the Data for Training and Simulation
% The function PREPARETS prepares time series 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,X,{},T);% Set up 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);
rmse=sqrt(mse(errors));
performance = perform(net,targets,outputs);
% View the Network
view(net)
% 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,X,{},T);
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,X,{},T);
ys = nets(xs,xis,ais);
earlyPredictPerformance = perform(nets,ts,ys);
%% 5. Multi-step ahead prediction
inputSeriesPred = [X(end-1:end),XVal];
targetSeriesPred = [T(end-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');
Best Answer