amazon-science

    amazon-science/chronos-forecasting

    Chronos: Pretrained Models for Time Series Forecasting

    ai
    nlp
    llm
    machine-learning
    deep-learning
    artificial-intelligence
    forecasting
    foundation-models
    huggingface
    huggingface-transformers
    Python
    Apache-2.0
    6.2K stars
    761 forks
    6.2K GitHub watchers
    Updated Nov 2, 2026
    View on GitHub

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    Where it stands

    Rank, growth pace and release cadence relative to everything GStars tracks.

    1 release in the last 90 days, the latest 54 days ago, with 20 contributors on GitHub.

    Gained 32% more stars in the last 90 days than in the 90 days before.

    Use Cases & Benefits

    • Provides pretrained models for accurate and efficient time series forecasting using advanced transformer architectures.
    • Delivers state-of-the-art zero-shot forecasting performance with models that are significantly faster and more memory-efficient than traditional approaches.
    • Use for forecasting univariate and multivariate time series with or without covariates in domains like energy price prediction.
    • Use for deploying scalable forecasting endpoints on cloud platforms such as Amazon SageMaker for production-grade inference.
    • Use for benchmarking and comparing forecasting models using integrated datasets and evaluation tools from the Chronos ecosystem.

    Should you depend on it?

    License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.

    License

    Apache 2.0

    Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.

    Community files

    • README: present
    • Contributing guide: present
    • Code of conduct: present

    Watch-outs

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    From the README

    This package provides an interface to the Chronos family of pretrained time series forecasting models. The following model types are supported.

    Excerpt from the amazon-science/chronos-forecasting README on GitHub (Apache-2.0 license). © its authors.Read the full README on GitHub

    Daily snapshots

    Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots

    Forks

    761+80

    Open issues

    42+10

    Compared with peers in AI

    Fastest-growing repositories GStars tracks in this cohort for the week of Nov 1, by 7-day star growth

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