amazon-science/chronos-forecasting

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✨ Introduction

Available Models

📈 Usage

pip install chronos-forecasting

Tip

For production use, we recommend deploying Chronos-2 to Amazon SageMaker. Two options:

• AutoGluon-Cloud (recommended) — high-level Python API. Pandas DataFrames in, forecasts out. Real-time, serverless, and batch inference out of the box.
• SageMaker JumpStart — production-ready real-time endpoints on CPU or GPU, ready to integrate into your existing AWS workflows.

Forecasting

import pandas as pd  # requires: pip install 'pandas[pyarrow]'
from chronos import Chronos2Pipeline

pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda")

# Load historical target values and past values of covariates
context_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/train.parquet")

# (Optional) Load future values of covariates
test_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/test.parquet")
future_df = test_df.drop(columns="target")

# Generate predictions with covariates
pred_df = pipeline.predict_df(
    context_df,
    future_df=future_df,
    prediction_length=24,  # Number of steps to forecast
    quantile_levels=[0.1, 0.5, 0.9],  # Quantile for probabilistic forecast
    id_column="id",  # Column identifying different time series
    timestamp_column="timestamp",  # Column with datetime information
    target="target",  # Column(s) with time series values to predict
)

import matplotlib.pyplot as plt  # requires: pip install matplotlib

ts_context = context_df.set_index("timestamp")["target"].tail(256)
ts_pred = pred_df.set_index("timestamp")
ts_ground_truth = test_df.set_index("timestamp")["target"]

ts_context.plot(label="historical data", color="xkcd:azure", figsize=(12, 3))
ts_ground_truth.plot(label="future data (ground truth)", color="xkcd:grass green")
ts_pred["predictions"].plot(label="forecast", color="xkcd:violet")
plt.fill_between(
    ts_pred.index,
    ts_pred["0.1"],
    ts_pred["0.9"],
    alpha=0.7,
    label="prediction interval",
    color="xkcd:light lavender",
)
plt.legend()

Example Notebooks

📝 Citation

@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}
}

@article{ansari2025chronos2,
  title        = {Chronos-2: From Univariate to Universal Forecasting},
  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},
  journal      = {arXiv preprint arXiv:2510.15821},
  year         = {2025},
  url          = {https://arxiv.org/abs/2510.15821}
}

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