TimesFM is a decoder-based foundational model pre-trained on large-scale time series datasets, boasting 20 billion parameters. Although smaller in scale compared to large language models, it demonstrates near-state-of-the-art zero-shot performance on multiple unseen datasets across various domains and time scales. TimesFM provides outstanding unseen time series predictions without the need for additional training.