Hey 
not sure if extremely trivial or really tricky.
In the end I want a machine that generates a time series without further input based on training data, generating a new time series every time.
I want this to be based on a transformer.
I want it trained with data looking like this:
2023-07-03 14:19:48,GOOD
2023-07-04 13:59:07,GOOD
2023-07-05 01:58:54,GOOD
2023-07-05 03:30:05,BAD
2023-07-05 05:17:43,BAD
2023-07-06 05:35:34,GOOD
2023-07-07 14:06:03,GOOD
2023-07-08 21:16:05,BAD
with “GOOD” and “BAD” being the state of the system which is likely dependent on the time series data up to that point. I have a lot of data and it’s data points like the one above with maybe a hundred rows of data on average for a few thousand systems. Every system is independent of all others but all are identical.
I do not want to train only on “GOOD” as this would leave out a lot of valueable data …
Is there a way to train a time series transformer with both data that leads to GOOD as well as BAD outcomes, so it would generate time series from scratch which are unlikely to have BAD outcomes?
Thank you!!