TimesFM is a 200M parameter Transformer model for time-series forecasting, achieving zero-shot performance comparable to supervised-learning models.
TimesFM uses a decoder-only transformer architecture, pre-trained on real-world and synthetic data, outperforming traditional statistical methods and deep learning models.