amazon/chronos-2

Chronos-2

Update Jun 5, 2026: ☁️ Deploy Chronos-2 on AWS with AutoGluon-Cloud. Real-time, serverless, or batch inference in 3 lines of code — pandas DataFrames in, forecasts out. Check out the new deployment guide.

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.

Links

• ☁️ Deploy on SageMaker with AutoGluon-Cloud (recommended)
• 🚀 Deploy on SageMaker with JumpStart
• 📄 Technical report
• 💻 GitHub
• 📘 Example notebook
• 📰 Amazon Science Blog

Overview

| Capability | Chronos-2 | Chronos-Bolt | Chronos | |------------|-----------|--------------|----------| | Univariate Forecasting | ✅ | ✅ | ✅ | | Cross-learning across items | ✅ | ❌ | ❌ | | Multivariate Forecasting | ✅ | ❌ | ❌ | | Past-only (real/categorical) covariates | ✅ | ❌ | ❌ | | Known future (real/categorical) covariates | ✅ | 🧩 | 🧩 | | Max. Context Length | 8192 | 2048 | 512 | | Max. Prediction Length | 1024 | 64 | 64 |

🧩 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.

Running the model locally

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

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
future_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/test.parquet").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], # Quantiles 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
)

Production use on Amazon SageMaker

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

• AutoGluon-Cloud (recommended) — minimal setup with a high-level Python API: pass a pandas DataFrame in, get forecasts back. Supports real-time, serverless, and batch inference out of the box.
• SageMaker JumpStart — fine-grained control over the deployment configuration. JSON request/response payloads only; serverless inference and batch prediction require additional setup.

☁️ AutoGluon-Cloud

Install AutoGluon-Cloud:

pip install autogluon.cloud>=0.5.0

Make predictions from a pandas DataFrame

from autogluon.cloud import TimeSeriesFoundationModel

model = TimeSeriesFoundationModel(model_name="chronos-2")

# Batch prediction
forecast_df = model.predict(df, prediction_length=24)

# Deploy & invoke a real-time endpoint
endpoint = model.deploy(instance_type="ml.g5.xlarge")
forecast_df = endpoint.predict(df, prediction_length=24)

For more details (e.g. serverless endpoints, covariate-aware forecasting), see the full deployment guide.

🚀 SageMaker JumpStart

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

pip install -U 'sagemaker<3'

Deploy an inference endpoint to SageMaker.

from sagemaker.jumpstart.model import JumpStartModel

model = JumpStartModel(
model_id="pytorch-forecasting-chronos-2",
instance_type="ml.g5.2xlarge",
)
predictor = model.deploy()

Now you can send time series data to the endpoint in JSON format.

payload = {
"inputs": [
{"target": [1.0, 2.5, ..., 12.3]}
],
"parameters": {
"prediction_length": 24,
}
}
forecast = predictor.predict(payload)["predictions"]

For more details about the endpoint API, check out the example notebook.

Training data

More details about the training data are available in the technical report.

• Subset of Chronos Datasets (excluding test portion of datasets that overlap with GIFT-Eval)
• Subset of GIFT-Eval Pretrain
• Synthetic univariate and multivariate data

Citation

If you…