I read this as basically finding a language representation of timeseries data that would be understood by LLMs better than feeding raw records. I'm guessing it tokenizes very similarly to any other LLM. Perhaps I misread, though.

This is the modern day equivalent of "every signal is an image" after the initial success of deep learning algorithms in image classification tasks. It is just the academia chasing the hype train...