MATLAB: Neural Network – Multi Step Ahead Prediction

Deep Learning Toolboxforecastin time series (ann)narxnarxnettutorial

Hi all, please I need your help !
I've read all the posts here about Time Series Forecasting but still can't figure it out ! I'm drained.. *:-(*
I've a NARX neural network with 10 hidden neurons and 2 delays. As input I have a 510×5 (called Inputx) and as output I have a 510×1 (called Target).
I want to forecast 10 days ahead but it's really not working…
I tried the following code but I'm stuck now. *:-(*
Would you mind to help me ? *Some code will be awesome. :-(*
***////////////////////////////////////////////******** ***/////////////////////////////////////////// ******
inputSeries = tonndata(Inputx,false,false);
targetSeries = tonndata(Target,false,false);
netc = closeloop(net);
netc.name = [net.name ' - Closed Loop'];
[xc,xic,aic,tc] = preparets(netc,inputSeries,{},targetSeries);
yc = netc(xc,xic,aic);
***////////////////////////////////////////////******** ***/////////////////////////////////////////// ******

Best Answer

Hi Jack,
When using narxnet, the network performs only a one-step ahead prediction after it has been trained. Therefore, you need to use closeloop to perform a multi-step-ahead prediction and turn the network into parallel configuration.
Take a look at this example for a multi-step-ahead prediction, N steps. This uses the dataset magdata.mat which is available in the Neural Network Toolbox. Also, some of the inputs will be used for performing the multi-step-ahead prediction, and results validated with the original data. I hope the comments help to understand.
Edited in September 2015 to simplify step 5
%% 1. Importing data
S = load('magdata');
X = con2seq(S.u);
T = con2seq(S.y);
%% 2. Data preparation
N = 300; % 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);
% 2nd group: this is the new data used for simulation. inputSeriesVal will 
% be used for predicting new targets. targetSeriesVal will be used for
% network validation after prediction
inputSeriesVal  = X(end-N+1:end);
targetSeriesVal = T(end-N+1:end); % This is generally not available
%% 3. Network Architecture
delay = 2;
neuronsHiddenLayer = 10;
% Network Creation
net = narxnet(1:delay,1:delay,neuronsHiddenLayer);
%% 4. Training the network
[Xs,Xi,Ai,Ts] = preparets(net,inputSeries,{},targetSeries); 
net = train(net,Xs,Ts,Xi,Ai);
view(net)
Y = net(Xs,Xi,Ai); 
% Performance for the series-parallel implementation, only 
% one-step-ahead prediction
perf = perform(net,Ts,Y);
%% 5. Multi-step ahead prediction
[Xs1,Xio,Aio] = preparets(net,inputSeries(1:end-delay),{},targetSeries(1:end-delay));
[Y1,Xfo,Afo] = net(Xs1,Xio,Aio);
[netc,Xic,Aic] = closeloop(net,Xfo,Afo);
[yPred,Xfc,Afc] = netc(inputSeriesVal,Xic,Aic);
multiStepPerformance = perform(net,yPred,targetSeriesVal);
view(netc)
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')
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