Chronos Bolt Mini

🚀 Chronos-Bolt⚡ (Mini)

Chronos-Bolt is a family of pretrained time series forecasting models designed for zero-shot forecasting. It's built on the T5 encoder-decoder architecture and trained on nearly 100 billion time series observations. It divides historical time series context into patches of multiple observations and inputs them into the encoder. The decoder then directly generates quantile forecasts for multiple future steps, a method called direct multi-step forecasting. Compared to the original Chronos models of the same size, Chronos-Bolt models are more accurate, up to 250 times faster, and 20 times more memory-efficient.

🚀 Quick Start

Update Feb 14, 2025

Chronos-Bolt models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code.

✨ Features

• High Performance: Significantly faster and more accurate than the original Chronos models.
• Multiple Sizes: Available in different sizes to meet various needs.
• Zero-shot Forecasting: Can perform forecasting without prior exposure to specific datasets.
• Deployment Flexibility: Can be deployed to both CPU and GPU instances and supports forecasting with covariates.

📦 Installation

Install the required dependencies:

pip install autogluon

Update the SageMaker SDK to access the latest models:

pip install -U sagemaker

💻 Usage Examples

Basic Usage - Zero-shot inference with Chronos-Bolt in AutoGluon

from autogluon.timeseries import TimeSeriesPredictor, TimeSeriesDataFrame

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

predictor = TimeSeriesPredictor(prediction_length=48).fit(
    df,
    hyperparameters={
        "Chronos": {"model_path": "autogluon/chronos-bolt-mini"},
    },
)

predictions = predictor.predict(df)

Advanced Usage - Deploying a Chronos-Bolt endpoint to SageMaker

from sagemaker.jumpstart.model import JumpStartModel

model = JumpStartModel(
    model_id="autogluon-forecasting-chronos-bolt-base",
    instance_type="ml.c5.2xlarge",
)
predictor = model.deploy()

Send time series data to the endpoint in JSON format:

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv")

payload = {
    "inputs": [
        {"target": df["#Passengers"].tolist()}
    ],
    "parameters": {
        "prediction_length": 12,
    }
}
forecast = predictor.predict(payload)["predictions"]

For more advanced features such as fine-tuning and forecasting with covariates, check out this tutorial. For more details about the endpoint API, check out the example notebook.

📚 Documentation

Performance

Inference Time Comparison

The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.

Forecasting Performance

The following plot reports the probabilistic and point forecasting performance of Chronos-Bolt in terms of the Weighted Quantile Loss (WQL) and the Mean Absolute Scaled Error (MASE), respectively, aggregated over 27 datasets (see the Chronos paper for details on this benchmark).

Model Sizes

Chronos-Bolt models are available in the following sizes:

📄 License

This project is licensed under the Apache-2.0 License.

📚 Citation

If you find Chronos or Chronos-Bolt models useful for your research, please consider citing the associated paper:

@article{ansari2024chronos,
    title={Chronos: Learning the Language of Time Series},
    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},
    journal={Transactions on Machine Learning Research},
    issn={2835-8856},
    year={2024},
    url={https://openreview.net/forum?id=gerNCVqqtR}
}