Update Dec 30, 2025: ☁️ Deploy Chronos-2 on Amazon SageMaker. New guide covers real-time GPU and CPU inference, serverless endpoints (run on demand, no idle costs), and batch transform for large-scale forecasting.
Chronos-2 is a 120M-parameter, encoder-only time series foundation model for zero-shot forecasting.
It supports univariate, multivariate, and covariate-informed tasks within a single architecture.
Inspired by the T5 encoder, Chronos-2 produces multi-step-ahead quantile forecasts and uses a group attention mechanism for efficient in-context learning across related series and covariates.
Trained on a combination of real-world and large-scale synthetic datasets, it achieves state-of-the-art zero-shot accuracy among public models on fev-bench, GIFT-Eval, and Chronos Benchmark II.
Chronos-2 is also highly efficient, delivering over 300 time series forecasts per second on a single A10G GPU and supporting both GPU and CPU inference.
🧩 Chronos & Chronos-Bolt do not natively support future covariates, but they can be combined with external covariate regressors (see AutoGluon tutorial). This only models per-timestep effects, not effects across time. In contrast, Chronos-2 supports all covariate types natively.
Usage
Local usage
For experimentation and local inference, you can use the inference package.
Install the package
pip install "chronos-forecasting>=2.0"
Make zero-shot predictions using the pandas API
python
1import pandas as pd # requires: pip install 'pandas[pyarrow]'2from chronos import Chronos2Pipeline
34pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda")56# Load historical target values and past values of covariates7context_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/train.parquet")89# (Optional) Load future values of covariates10test_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/test.parquet")11future_df = test_df.drop(columns="target")1213# Generate predictions with covariates14pred_df = pipeline.predict_df(15 context_df,16 future_df=future_df,17 prediction_length=24,# Number of steps to forecast18 quantile_levels=[0.1,0.5,0.9],# Quantiles for probabilistic forecast19 id_column="id",# Column identifying different time series20 timestamp_column="timestamp",# Column with datetime information21 target="target",# Column(s) with time series values to predict22)
Deploying a Chronos-2 endpoint to SageMaker
For production use, we recommend deploying Chronos-2 endpoints to Amazon SageMaker.
First, update the SageMaker SDK to make sure that all the latest models are available.
If you find Chronos-2 useful for your research, please consider citing the associated paper:
@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},
year = {2025},
url = {https://arxiv.org/abs/2510.15821}
}
