Do I have to scale/normalize my training data for LSTM Classification, even if I only have one feature?
Normalizing the features ensures that they take on reasonable values say between -3 and +3. This ensures that you don't run into a numeric overflow or under flow issues in your network. For e.g. just see what value np.exp(312) or np.exp(-312) takes on.
where, 312 is a value of the bitrate in your observation.
Certain activation functions such as the sigmoid might run into numerical precision issues if your data is not normalized. So, in this case it doesn't matter if your data contains only 1 feature.