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 ☁️ 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 . is a 120M-parameter, encoder-only time series foundation model for zero-shot forecasting. It supports , , and 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 among public models on , , and . Chronos-2 is also , delivering over 300 time series forecasts per second on a single A10G GPU and supporting both . ## Links - ☁️ (recommended) - 🚀 - 📄 - 💻 - 📘 - 📰 ## 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 ). 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 . Install the package Make zero-shot predictions using the API ## Production use on Amazon SageMaker For production use, we recommend deploying Chronos-2 to Amazon SageMaker. There are two options: - (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. - — 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: Make predictions from a pandas DataFrame For more details (e.g. serverless endpoints, covariate-aware forecasting), see the . ### 🚀 SageMaker JumpStart First, update the SageMaker SDK to make sure that all the latest models are available. Deploy an inference endpoint to SageMaker. Now you can send time series data to the endpoint in JSON format. For more details about the endpoint API, check out the . ## Training data More details about the training data are available in the . - Subset of (excluding test portion of datasets that overlap with GIFT-Eval) - Subset of - Synthetic univariate and multivariate data ## Citation If you find Chronos-2 useful for your research, please consider citing the associated paper:
python 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 )
AutoGluon-Cloud
SageMaker JumpStart
pip install autogluon.cloud>=0.5.0
python 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)
@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} }
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.
