Time Series Forecasting
Chronos
Safetensors
t5
time series
forecasting
pretrained models
foundation models
time series foundation models
time-series
Instructions to use amazon/chronos-bolt-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Chronos
How to use amazon/chronos-bolt-tiny with Chronos:
pip install chronos-forecasting
import pandas as pd from chronos import BaseChronosPipeline pipeline = BaseChronosPipeline.from_pretrained("amazon/chronos-bolt-tiny", device_map="cuda") # Load historical data context_df = pd.read_csv("https://autogluon.s3.us-west-2.amazonaws.com/datasets/timeseries/misc/AirPassengers.csv") # Generate predictions pred_df = pipeline.predict_df( context_df, prediction_length=36, # Number of steps to forecast quantile_levels=[0.1, 0.5, 0.9], # Quantiles for probabilistic forecast id_column="item_id", # Column identifying different time series timestamp_column="Month", # Column with datetime information target="#Passengers", # Column(s) with time series values to predict ) - Notebooks
- Google Colab
- Kaggle
Update model card with Chronos-2 paper link, GitHub repo, `transformers` tag, and full citation
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by nielsr HF Staff - opened
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license: apache-2.0
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pipeline_tag: time-series-forecasting
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tags:
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---
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# Chronos-Bolt⚡ (Tiny)
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## Citation
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If you find Chronos or Chronos-Bolt models useful for your research, please consider citing the associated
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```
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@article{ansari2024chronos,
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year={2024},
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url={https://openreview.net/forum?id=gerNCVqqtR}
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}
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```
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## License
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This project is licensed under the Apache-2.0 License.
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---
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license: apache-2.0
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pipeline_tag: time-series-forecasting
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library_name: transformers
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tags:
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- time series
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- forecasting
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- pretrained models
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- foundation models
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- time series foundation models
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- time-series
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---
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The Chronos-Bolt (Tiny) model was presented in the paper [Chronos-2: From Univariate to Universal Forecasting](https://huggingface.co/papers/2510.15821).
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The code for this model can be found in the [Chronos Forecasting GitHub repository](https://github.com/amazon-science/chronos-forecasting).
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---
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# Chronos-Bolt⚡ (Tiny)
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## Citation
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If you find Chronos or Chronos-Bolt models useful for your research, please consider citing the associated papers:
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```
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@article{ansari2024chronos,
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year={2024},
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url={https://openreview.net/forum?id=gerNCVqqtR}
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}
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@article{ansari2025chronos2,
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title = {Chronos-2: From Univariate to Universal Forecasting},
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author = {Abdul Fatir Ansari and Oleksandr Shchur and Jaris Küken and Andreas Auer and Boran Han and Pedro Mercado and Syama Sundar Rangapuram and Huibin Shen and Lorenzo Stella and Xiyuan Zhang and Mononito Goswami and Shubham Kapoor and Danielle C. Maddix and Pablo Guerron and Tony Hu and Junming Yin and Nick Erickson and Prateek Mutalik Desai and Hao Wang and Huzefa Rangwala and George Karypis and Yuyang Wang and Michael Bohlke-Schneider},
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journal = {arXiv preprint arXiv:2510.15821},
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year = {2025},
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url = {https://arxiv.org/abs/2510.15821}
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}
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```
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## License
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This project is licensed under the Apache-2.0 License.
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