I'm answering instead of commenting because of the reputation points hindrance.
The above answer by @couturierc does not address the issue and it's amazing how often people use that to reply, instead of just... suppressing their thoughts.
Regressors are independent variables that are used as influencers for the output. Your case — and mine! — are to predict categorical variables, meaning that the category itself is the output. And you are absolutely right, Brian, 99.7% of the TSA literature focuses on predicting continuous values, such as temperatures or stock values.
My problem is predicting sensor data, which can be exclusively on or off. By exclusively, I mean that I have a set of sensors and at any given time only one of them can be on. So, my data is by nature one-hot. To make matters all the funnier, my timestamps are not evenly spaced.
The furthest I could go was to use
traces and also some playing with
Prophet, but so far all I could come up with is independent category value prediction. In fact, Prophet makes it very obvious that one value is to be analysed at a time.
Alternatives suggested to me include label encoding with scikit-learn (Attention! New way to do this with
from sklearn.compose import ColumnTransformer), but this poses the ancient question of having numeric values on the categories.
Since your post is from 9 months ago, if you have come up with some solution, I would love to hear about it.