Train a Time Series Prediction Transformer Model on Good/Bad Outcomes

Hi,

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 model.

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 valuable data …

So the task is 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 that are unlikely to have BAD outcomes.

An online platform for training of your choice should be used (if possible, suggest in your bid or PM) and inference should be possible via API or some web endpoint. There is budget for training of course.

I expect this to take maybe a week, as very rough estimate. Lot's of follow up work available.

Thank you!