To modify the chickenpox example for a multi-input, single-output time series prediction task, you must configure your input and network architecture to effectively handle multiple input features (the initial three time series) and generate a single output (the fourth time series).
Kindly refer to the following network architecture for understanding.
- Sequence Input Layer: Set InputSize to 3.
- LSTM Layer: Use a suitable number of hidden units (e.g., 50).
- Fully Connected Layer: Set the number of outputs to 1.
- Regression Layer: For predicting continuous values.
Kindly refer to the following code snippet to understand the implementation of the above architecture.
% Example data preparation
XTrain = [TS1; TS2; TS3];
YTrain = TS4;
% Define the network architecture
layers = [
sequenceInputLayer(3) % 3 input features
lstmLayer(50, 'OutputMode', 'sequence') % Example LSTM layer
fullyConnectedLayer(1) % Output size of 1
regressionLayer
];
% Training options
options = trainingOptions('adam', ...
'MaxEpochs', 100, ...
'MiniBatchSize', 20, ...
'Plots', 'training-progress', ...
'Verbose', 0);
% Train the network
net = trainNetwork(XTrain, YTrain, layers, options);
% Predict and evaluate
YPred = predict(net, XTest); % Replace XTest with your test data
For more information about “lstmLayer” kindly refer following MathWorks documentation.
I hope this will be helpful.
