TimesFM (Google Research)
Pretrained time series foundation model for long-horizon forecasting across diverse scientific domains including climate variables, biomedical signals, and physical observations; decoder-only Transformer architecture with strong zero-shot generalization (19.8K+ stars, Apache 2.0, 2024-2025)
README
TimesFM TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting. Paper: A decoder-only foundation model for time-series forecasting, ICML 2024. All checkpoints: TimesFM Hugging Face Collection. Google Research blog. TimesFM in Google 1P Products: BigQuery ML: Enterprise level SQL queries for scalability and reliability. Google Sheets: For your daily spreadsheet. Vertex Model Garden: Dockerized endpoint for…
- Repository
- github.com/google-research/timesfm
Source attribution
- Awesome AI for Science — github.com/google-research/timesfm
- GitHub — github.com/google-research/timesfm
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Pretrained time series foundation model for zero-shot forecasting across diverse scientific and real-world domains; tokenizes continuous time series into discrete bins to train transformer language models on large-scale corpora, achieving strong zero-shot generalization and competitive performance with task-specific supervised models on climate, energy, and health benchmarks (5.3K+ stars, Apache 2.0, 2024-2026)