🚀 News

• 5 Jun 2026: ☁️ Deploy Chronos-2 to AWS with AutoGluon-Cloud — real-time, serverless, or batch inference in 3 lines of code, pandas DataFrames in, forecasts out. See the new deployment guide.
• 30 Dec 2025: 🧠 Deploy Chronos-2 to AWS with Amazon SageMaker JumpStart — production-ready real-time endpoints on CPU or GPU, ready to integrate into your existing AWS workflows. See the deployment tutorial.
• 20 Oct 2025: 🚀 Chronos-2 released. It offers zero-shot support for univariate, multivariate, and covariate-informed forecasting tasks. Chronos-2 achieves the best performance on fev-bench, GIFT-Eval and Chronos Benchmark II amongst pretrained models. Check out this notebook to get started with Chronos-2.
• 12 Dec 2024: 📊 We released fev, a lightweight package for benchmarking time series forecasting models based on the Hugging Face datasets library.
• 26 Nov 2024: ⚡️ Chronos-Bolt models released on HuggingFace. Chronos-Bolt models are more accurate (5% lower error), up to 250x faster and 20x more memory efficient than the original Chronos models of the same size!
• 13 Mar 2024: 🚀 Chronos paper and inference code released.

✨ Introduction

This package provides an interface to the Chronos family of pretrained time series forecasting models. The following model types are supported.

• Chronos-2: Our latest model with significantly enhanced capabilities. It offers zero-shot support for univariate, multivariate, and covariate-informed forecasting tasks. Chronos-2 delivers state-of-the-art zero-shot performance across multiple benchmarks (including fev-bench and GIFT-Eval), with the largest improvements observed on tasks that include exogenous features. It also achieves a win rate of over 90% against Chronos-Bolt in head-to-head comparisons. To learn more about Chronos, check out the technical report.
• Chronos-Bolt: A patch-based variant of Chronos. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. To learn more about Chronos-Bolt, check out this blog post.
• Chronos: The original Chronos family which is based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. To learn more about Chronos, check out the publication.

Available Models