Ok now I’m confused
Let me backtrack a bit to make sure I understand what you’re saying. You’re right because I’m reshaping my timeseries into a bunch of sequences of length q and use each one to predict the “next” observation. My input (and ground-truth) data is organized sequentially (timesteps 1-20 to predict 21, 2-21 to predict 22 etc). I then run nn.LSTM model with input size (20, batch_size, 1) to predict that “next” value. Now, you’re saying this is memoryless, and you also mentioned using nn.LSTMCell to create a stateful lstm in an earlier reply, are these two things related? This is where my confusion lies because I can’t see how my setup is preventing cell state to be carried from one time step to the next (I guess I just assumed nn.LSTM would automagically do this because… its an LSTM ).