---
license: apache-2.0
pipeline_tag: time-series-forecasting
tags:
  - time series
  - forecasting
  - pretrained models
  - foundation models
  - time series foundation models
  - time-series
---

# Chronos-Bolt

Pre-release of Chronos-Bolt pretrained time series forecasting models. 

## Usage

A minimal example showing how to perform inference using Chronos-Bolt with AutoGluon:

```
pip install --pre autogluon
```

```python
from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame

df = TimeSeriesDataFrame("https://autogluon.s3.amazonaws.com/datasets/timeseries/m4_hourly/train.csv")

predictions = TimeSeriesPredictor().fit(
    df,
    hyperparameters={
        "Chronos": [
            {"model_path": "autogluon/chronos-bolt-base"},
        ]
    },
).predict(
    df
)
```

## Citation

If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815):

```
@article{ansari2024chronos,
  author  = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},
  title   = {Chronos: Learning the Language of Time Series},
  journal = {arXiv preprint arXiv:2403.07815},
  year    = {2024}
}
```

## License

This project is licensed under the Apache-2.0 License.