Chronos Bolt Tiny

🚀 Chronos-Bolt⚡ (Tiny)

Chronos-Bolt is a family of pretrained time series forecasting models designed for zero-shot forecasting. It's based 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 known as direct multi - step forecasting. Compared with 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

Zero - shot inference with Chronos - Bolt in AutoGluon

Install the required dependencies:

pip install autogluon

Forecast with the Chronos - Bolt model:

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-tiny"},
    },
)

predictions = predictor.predict(df)

For more advanced features such as fine - tuning and forecasting with covariates, check out this tutorial.

Deploying a Chronos - Bolt endpoint to SageMaker

First, update the SageMaker SDK to make sure that all the latest models are available:

pip install -U sagemaker

Deploy an inference 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()

Now you can 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"]

Chronos - Bolt models can be deployed to both CPU and GPU instances. These models also support forecasting with covariates. For more details about the endpoint API, check out the [example notebook](https://github.com/amazon - science/chronos - forecasting/blob/main/notebooks/deploy - chronos - bolt - to - amazon - sagemaker.ipynb).

✨ Features

• 🚀 Update Feb 14, 2025: Chronos - Bolt models are now available on Amazon SageMaker JumpStart! Check out the [tutorial notebook](https://github.com/amazon - science/chronos - forecasting/blob/main/notebooks/deploy - chronos - bolt - to - amazon - sagemaker.ipynb) to learn how to deploy Chronos endpoints for production use in a few lines of code.
• High - performance: Chronos - Bolt models are not only significantly faster but also more accurate than the original Chronos models.

📦 Installation

Install AutoGluon

pip install autogluon

Update SageMaker SDK

pip install -U sagemaker

💻 Usage Examples

Basic Usage

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-tiny"},
    },
)

predictions = predictor.predict(df)

Advanced Usage

from sagemaker.jumpstart.model import JumpStartModel

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

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"]

📚 Documentation

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](https://github.com/amazon - science/chronos - forecasting/blob/main/notebooks/deploy - chronos - bolt - to - amazon - sagemaker.ipynb).

🔧 Technical Details

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.

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). Remarkably, despite having no prior exposure to these datasets during training, the zero - shot Chronos - Bolt models outperform commonly used statistical models and deep learning models that have been trained on these datasets (highlighted by *). Furthermore, they also perform better than other FMs, denoted by a +, which indicates that these models were pretrained on certain datasets in our benchmark and are not entirely zero - shot. Notably, Chronos - Bolt (Base) also surpasses the original Chronos (Large) model in terms of the forecasting accuracy while being over 600 times faster.

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}
}