TimesFM is a pretrained decoder-only transformer foundation model for time-series forecasting, developed by Google Research. The repository provides multiple model versions (v1.0, v2.0, v2.5, v3.0) with implementations in PyTorch, Flax/JAX, and MLX (Apple Silicon). It supports zero-shot univariate and multivariate forecasting with covariates, quantile outputs, and various preprocessing options (RevIN normalization, linear detrending, symmetric averaging). The codebase includes a high-level forecaster API, CLI scripts for CSV forecasting and system checks, fine-tuning examples (LoRA/DoRA), and comprehensive benchmarks (GIFT-Eval, TIME, FEV). While primarily an application library, it also contains research-oriented components like benchmark runners and notebooks, making it a hybrid project with a strong application focus.