TL;DR: * Scale the time series data and quantize the floating point values into B bins. * Each bin becomes a corresponding token id in a vocabulary of B embed

TL;DR: * Scale the time series data and quantize the floating point values into B bins. * Each bin becomes a corresponding token id in a vocabulary of B embeddings. * Train a small LLM to predict the next token id given a sequence of token ids. * At each time step, the LLM gives you a probability distribution over B bins.