Hi byungchan,
It is my understanding that you have questions on LSTM with regard to having long sequence length.
1) While it is technically possible to use a very long sequence length for LSTM, it is not always the most optimal approach. Using very long sequences can lead to many challenges such as vanishing gradients, which can make it difficult to learn dependencies across the long sequence. Additionally, training on such long sequences requires a large amount of memory which may become impossible for GPU with low memory capacity resulting in out of memory errors. Splitting the sequence into shorter sub-sequences is a common approach to address these challenges.
In your specific case, it may be better to split the long time series into shorter sequences and experiment with different lengths to optimize your model's performance. This would also make the training faster and more tractable since you would only be training your model on shorter sequences at a time. It is up to you to determine how best to split and format your data based on your model's requirements and the specific features of your data.
2) Yes, you can change the sequence length parameter in your LSTM model to control the number of time steps the network processes input data at a time. This can be done by modifying the training options passed to the trainNetwork function.
Specifically, you can set the sequence length to match the number of time steps you want to focus on in your data. If you want to focus on the last 70 to 80 time steps, and each time step corresponds to 0.1 seconds, you can set the sequence length to be 7 or 8, depending on the size of your input data.
% sample implementation
sequenceLength = 7;
miniBatchSize = 64;
numHiddenUnits = 100;
layers = [ ...
sequenceInputLayer(inputSize)
lstmLayer(numHiddenUnits,'OutputMode','last')
fullyConnectedLayer(outputSize)
softmaxLayer
classificationLayer];
options = trainingOptions('adam', ...
'MaxEpochs',100, ...
'MiniBatchSize',miniBatchSize, ...
'SequenceLength',sequenceLength, ... % set the sequence length here
'GradientThreshold',1, ...
'Shuffle','never', ...
'Plots','training-progress');
net = trainNetwork(XTrain,YTrain,layers,options);
Note that changing the sequence length parameter can affect the accuracy of your LSTM model.
For more information refer the documentation page MATLAB trainNetwork (mathworks.com)
