Hi Tony,
Yes, it's possible to apply a sliding window to your data and use a matrix as input for the sequenceInputLayer.
You can do the following updates.
- If your original data is in a 2D matrix of size (numSamples, numFeatures), reshape it into a 3D tensor of size (numSamples - windowSize + 1, windowSize, numFeatures). This creates the sliding window sequences.
- Change the inputSize property of the sequenceInputLayer to [windowSize, numFeatures]. This tells the layer to expect matrix inputs with the specified dimensions.
layers = [ ...
sequenceInputLayer([windowSize, numFeatures])
... % Rest of your code
];
MATLAB doesn't inherently know that your data is time series. It treats it as a 3D tensor with dimensions corresponding to samples, timesteps, and features.
The LSTM and GRU layers are designed to handle sequential data, so they can learn temporal patterns within your time series.
I hope it helps!
