Time-Series LLMs, Explained with t0-alpha

The article introduces t0-alpha, a decoder-style patch transformer designed for probabilistic time-series forecasting. It processes raw series by splitting them into 32-step patches, applying causal time-attention and group-attention layers, and generating future quantiles instead of single-point predictions. The model represents an application of LLM-like architectures to time-series data.