Yes, the time-series data made more sense to me as a beginner than the text data because I could think in terms of ‘samples’ and ‘variables’  I’ll try tackling text data next.

A sentence is nothing but a time series of words. The LSTM doesn’t care 

Follow up 1: Is there any reason I couldn’t have input_size > 1 ? What if my time series data has more than just a passengers variable? For instance each point in time might also have an average weather, or some such thing. Can I not use multiple variables to predict a single time series outcome?

You can of course have multiple features for each data point, i.e., input_size > 1.

follow up 2: Okay, so LSTM layer outputs/hidden aren’t worth trying to interpret? All of that data is transient and unit-less, not meant for human consumption? At least I understand the shape of it now!

Exactly.

follow up 3: I’m wondering why .view(-1, self.hidden_size) was used here, in this Udacity notebook 6 in their .forward() method?

I’ve looked at the notebook, and I would argue the setup is a bit different. If I understand your code correctly, your input are sequence of length 12 and the target is of length 1 (basically your one label). In the notebook, the target is also a sequence and of the same length as the input sequence, only shifted by one time step. That means with one example, say input=[1,2,3,4] and target=[2,3,4,5], they simulate a batch of 4 examples:

[1] => 2
[1,2] => 3
[1,2,3] => 4
[1,2,3,4] => 5

So there .view(-1, self.hidden_size) makes sense.
Can you post the shape of label as well as the code for the training including the part where you calculate the loss?