MATLAB: How can i enter more than one input in NARX model

narx with multiple input

Hi!

I want to train a NARX model, which have 1 target time series data and 3 input time series data. I tried to use the nnstool, but there I could use only 1 input data. How could I train my network with more than 1 input?

thanks, Roley

Best Answer

Hi Roland:

You have to arrange your 3 input series data into 1.

Please check the example "pH Neutralization Process" that comes with the system.

Run these commands and check afterwards the variable "inputSeries". This one contains two series as input.

load pH_dataset
inputSeries = phInputs;
targetSeries = phTargets;

More info here:

http://www.mathworks.ch/ch/help/nnet/gs/neural-network-time-series-prediction-and-modeling.html

Best regards. Fran.

Related Solutions

MATLAB: Multi step ahead forecasting with LSTM

Forecasting is basicaly sequence-to-sequence regression, let suppos that your entire sequence is data,

1. You divide data into train and test parts, you can specify the proportion as you wish:

numTimeStepsTrain = floor(0.9*numel(data));% 90% for training 10%for testing
dataTrain = data(1:numTimeStepsTrain+1);
dataTest = data(numTimeStepsTrain+1:end);

2. Preparing training data and response sequences by shifting data by one time step, such as for data(t) the response will be data(t+1)

XTrain = dataTrain(1:end-1);
YTrain = dataTrain(2:end);

3. Preparing the network and training hyperparameters, then train the network using training data and training responses

numFeatures = 1;
numResponses = 1;
numHiddenUnits = 200;
layers = [ ...
    sequenceInputLayer(numFeatures)
    lstmLayer(numHiddenUnits)
    fullyConnectedLayer(numResponses)
    regressionLayer];
options = trainingOptions('adam', ...
    'MaxEpochs',250, ...
    'GradientThreshold',1, ...
    'InitialLearnRate',0.005, ...
    'Verbose',0, ...
    'Plots','training-progress');
net = trainNetwork(XTrain,YTrain,layers,options);

3. Now you can forecast 1, 2, 3 or 4 steps ahead using predictAndUpdateState function, since you use predicted values to update the network state and you don’t use actual values contained in dataTest for this, you can make predictions on any time step number

net = predictAndUpdateState(net,XTrain);
[net,YPred] = predictAndUpdateState(net,YTrain(end));
stepsAhead = 4; % you can use 1,2,3,4 on any value of steps ahead
for i = 2:stepsAhead+1
    [net,YPred(:,i)] = predictAndUpdateState(net,YPred(:,i-1));
end

MATLAB: Problem with closeloop narx

This is what an open loop looks like during training:

http://www.mathworks.com/help/nnet/ug/dynamic_narx_open.gif

Since it is NARX you will have one y(t) as the target and x(t) as the eXogenous input.

When you close the loop as follows:

http://www.mathworks.com/help/nnet/ug/dynamic_narx_closed.gif

you will ONLY need to provide the eXogenous inputs, y(t) will be predicted by the output.

Scroll down to see the code of how this is done:

http://www.mathworks.com/help/nnet/gs/time-series-prediction.html

If you don't have an eXogenous input x(t) then you need to be using NAR network.

Best Answer

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MATLAB: Multi step ahead forecasting with LSTM

MATLAB: Problem with closeloop narx

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