I understand that you have to train a LSTM network for forcasting univariate time series. I created a sample input data based on the sizes mentioned in the question. I was not able to reproduce the error as I do not have access to the complete code for training. Please refer to the below code for training a LSTM network for time series forcasting. I have specified the 'OutputMode' to be 'last' as we only need the prediction for the final time step of the sequence.
% Sample data generation (replace with your actual data)
data = sin(0:0.01:100)'; % Example time series data
% Parameters
numTimeSteps = 20; % Number of timesteps in each input sequence
numSamples = 9600; % Total number of samples
numChannels = 1; % Univariate data (one feature per time step)
numResponses = 1; % Single output prediction
numHiddenUnits = 128; % Number of hidden units in LSTM
% Prepare input sequences and targets
X = cell(numSamples, 1);
Y = zeros(numSamples, 1);
for i = 1:numSamples
X{i} = data(i:i+numTimeSteps-1)'; % Each cell contains a 1x20 sequence
Y(i) = data(i+numTimeSteps); % Each target is a scalar
end
% Define LSTM network architecture
layers = [
sequenceInputLayer(numChannels, 'Name', 'input')
lstmLayer(numHiddenUnits, 'OutputMode', 'last', 'Name', 'lstm')
fullyConnectedLayer(numResponses, 'Name', 'fc')
regressionLayer('Name', 'output')
];
% Training options
options = trainingOptions('adam', ...
'MaxEpochs', 5, ...
'GradientThreshold', 1, ...
'InitialLearnRate', 0.01, ...
'LearnRateSchedule', 'piecewise', ...
'LearnRateDropFactor', 0.2, ...
'LearnRateDropPeriod', 10, ...
'Verbose', 0, ...
'Plots', 'training-progress');
% Train the network
net = trainNetwork(X, Y, layers, options);
For more information on "lstmLayer" please refer to the below mentioned link.
Also, for more information about LSTM, please refer to the below link.
Hope you find this information helpul.
