For my timeseries problem it seems obvious to use teacher forcing. For example in the case of predicting the new timestep in a real life scenario, I do have access to all the ground truths for all previous steps so using this information would paint a more accurate picture.
But, in my current implementation of a LSTM, I don't see an obvious way to implement teacher forcing. I have an lstm with hidden dimension of 20. After the lstm I apply a linear layer transforming the data to one dimensional. As the hidden dimension is 20, I don't see how I can input the one dimensional ground truth into the model. Is there something obvious I'm missing, or can someone point me to some related literature? Thanks!