--- title: "Chronos-2" kind: "Kit" category: "Business" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f1e85444-10b6-4178-a76d-741bfe468261" updated: "2026-11-12" --- # Chronos-2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Forecasts time series without any training on your data, covering single series, many related series and series with extra inputs. A 120M-parameter model from Amazon that runs on GPU or CPU. Chronos-2 is a kit in the Business category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by amazon ## About ### What it is Chronos-2 is a time series foundation model from Amazon that forecasts without training on your own data. One model handles a single series, several linked series and series with extra inputs such as prices or weather. It gives forecasts as quantiles, so you see a range rather than one number. ### Who it suits Analysts and developers who forecast numbers over time and want a ready model instead of building one per series. It suits teams with many related series, since the model learns from them together. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Energy | Market analysts | Forecast prices or load with extra inputs | Predict electricity prices 24 steps ahead, as the card's own example does | | Retail | Planning teams | Forecast demand across many related products | Predict sales for a group of items from past sales and promotions | | Finance | Data scientists | Produce forecast ranges for planning | Read the 0.1, 0.5 and 0.9 quantiles for a series | | Software | Developers | Add forecasting to an app with a few lines of Python | Pass a pandas DataFrame in and get forecasts back | You can skip it if you need to explain why a forecast moved. The model gives predictions, not reasons. ### Where to use it - Python on Windows, macOS and Linux, with the chronos-forecasting package - A single GPU or a CPU, since the card supports both - Amazon SageMaker, through AutoGluon-Cloud or JumpStart, for production | Use case | Who | What they get back | | --- | --- | --- | | Zero-shot forecast | Analysts | Quantile forecasts for the series you pass in | | Forecast with covariates | Data scientists | Predictions that use past and known future inputs | | Production serving | Platform teams | Real-time, serverless or batch inference on SageMaker | ### How to set it up You need Python and the chronos-forecasting package. - Python on Windows, macOS or Linux - The chronos-forecasting package, version 2.0 or newer - A GPU is optional, and the card's example selects cuda 1. Install Python if you do not have it. 2. Install the package with pip. 3. Load the pipeline and pass your data. 4. For production, deploy to SageMaker with AutoGluon-Cloud. ```bash pip install "chronos-forecasting>=2.0" ``` ```bash pip install autogluon.cloud>=0.5.0 ``` ### How to use it Load the pipeline from the model name, then call predict\_df on a pandas DataFrame of history. The DataFrame holds an id column, a timestamp column and the target values. Add a second DataFrame with known future covariates if you have one. ```python from chronos import Chronos2Pipeline pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda") 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 ) ``` ### Example You have hourly electricity prices and the known future values of a covariate. You ask for the next 24 steps at the 0.1, 0.5 and 0.9 quantiles. You get a table with a low, middle and high forecast for each step. ### For newcomers and experts A time series is a list of numbers over time, such as daily sales. This model reads your past numbers and predicts the next ones with no training. A first small step is to run the quickstart pattern on one series and look at the three quantile columns. It is an encoder-only model inspired by the T5 encoder. A group attention mechanism lets it learn across related series and covariates in context. On the fev-bench, GIFT-Eval and Chronos Benchmark II tests, the card puts its zero-shot accuracy at the top among public models. One A10G GPU reportedly handles more than 300 forecasts each second. | Capability | Chronos-2 | Chronos-Bolt | Chronos | | --- | --- | --- | --- | | Univariate forecasting | Yes | Yes | Yes | | Learning across items | Yes | No | No | | Multivariate forecasting | Yes | No | No | | Past-only covariates | Yes | No | No | | Known future covariates | Yes | Needs add-ons | Needs add-ons | Pass a DataFrame of past values → Add known future covariates if any → The model reads related series together → Get quantile forecasts per step Maximum context length in steps | Label | Value | | --- | --- | | Chronos-2 | 8192 steps | | Chronos-Bolt | 2048 steps | | Chronos | 512 steps | ### Good to know - The maximum prediction length is 1024 steps, against 64 for Chronos-Bolt and Chronos. - The older models can use external covariate regressors, which model effects per time step only. - The weights are about 478 MB in F32, with 119M parameters. - License: apache-2.0. - Cost to run: none named locally. A SageMaker deployment needs an AWS account, and the card gives no prices. - Data it reaches: the series you pass in, plus files downloaded from Hugging Face. - Not for: explaining the causes behind a forecast. ## README ````markdown # Chronos-2 Forecasts time series without any training on your data, covering single series, many related series and series with extra inputs. A 120M-parameter model from Amazon that runs on GPU or CPU. Made by amazon on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Chronos-2 is a time series foundation model from Amazon that forecasts without training on your own data. One model handles a single series, several linked series and series with extra inputs such as prices or weather. It gives forecasts as quantiles, so you see a range rather than one number. Who it suits: Analysts and developers who forecast numbers over time and want a ready model instead of building one per series. It suits teams with many related series, since the model learns from them together. ```table Industry | Who uses it | How they use it | Example Energy | Market analysts | Forecast prices or load with extra inputs | Predict electricity prices 24 steps ahead, as the card's own example does Retail | Planning teams | Forecast demand across many related products | Predict sales for a group of items from past sales and promotions Finance | Data scientists | Produce forecast ranges for planning | Read the 0.1, 0.5 and 0.9 quantiles for a series Software | Developers | Add forecasting to an app with a few lines of Python | Pass a pandas DataFrame in and get forecasts back ``` You can skip it if you need to explain why a forecast moved. The model gives predictions, not reasons. Where to use it: - Python on Windows, macOS and Linux, with the chronos-forecasting package - A single GPU or a CPU, since the card supports both - Amazon SageMaker, through AutoGluon-Cloud or JumpStart, for production ```table Use case | Who | What they get back Zero-shot forecast | Analysts | Quantile forecasts for the series you pass in Forecast with covariates | Data scientists | Predictions that use past and known future inputs Production serving | Platform teams | Real-time, serverless or batch inference on SageMaker ``` How to set it up: You need Python and the chronos-forecasting package. - Python on Windows, macOS or Linux - The chronos-forecasting package, version 2.0 or newer - A GPU is optional, and the card's example selects cuda 1. Install Python if you do not have it. 2. Install the package with pip. 3. Load the pipeline and pass your data. 4. For production, deploy to SageMaker with AutoGluon-Cloud. ```bash pip install "chronos-forecasting>=2.0" ``` ```bash pip install autogluon.cloud>=0.5.0 ``` How to use it: Load the pipeline from the model name, then call predict_df on a pandas DataFrame of history. The DataFrame holds an id column, a timestamp column and the target values. Add a second DataFrame with known future covariates if you have one. ```python from chronos import Chronos2Pipeline pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda") 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 ) ``` Example: You have hourly electricity prices and the known future values of a covariate. You ask for the next 24 steps at the 0.1, 0.5 and 0.9 quantiles. You get a table with a low, middle and high forecast for each step. For newcomers and experts: A time series is a list of numbers over time, such as daily sales. This model reads your past numbers and predicts the next ones with no training. A first small step is to run the quickstart pattern on one series and look at the three quantile columns. It is an encoder-only model inspired by the T5 encoder. A group attention mechanism lets it learn across related series and covariates in context. On the fev-bench, GIFT-Eval and Chronos Benchmark II tests, the card puts its zero-shot accuracy at the top among public models. One A10G GPU reportedly handles more than 300 forecasts each second. ```table Capability | Chronos-2 | Chronos-Bolt | Chronos Univariate forecasting | Yes | Yes | Yes Learning across items | Yes | No | No Multivariate forecasting | Yes | No | No Past-only covariates | Yes | No | No Known future covariates | Yes | Needs add-ons | Needs add-ons ``` ```flow Pass a DataFrame of past values Add known future covariates if any The model reads related series together Get quantile forecasts per step ``` ```chart Title: Maximum context length in steps Chronos-2: 8192 steps Chronos-Bolt: 2048 steps Chronos: 512 steps ``` Good to know: - The maximum prediction length is 1024 steps, against 64 for Chronos-Bolt and Chronos. - The older models can use external covariate regressors, which model effects per time step only. - The weights are about 478 MB in F32, with 119M parameters. - License: apache-2.0. - Cost to run: none named locally. A SageMaker deployment needs an AWS account, and the card gives no prices. - Data it reaches: the series you pass in, plus files downloaded from Hugging Face. - Not for: explaining the causes behind a forecast. ## On Hugging Face, 13 November 2026 - Task: time series forecasting - Library: chronos-forecasting - Parameters: 119M - Weights: about 478 MB in F32; about 60 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 8,097,695 - Likes: 575 - License: apache-2.0 - Quantized versions: 10 - Fine-tunes: 19 - Tags: chronos-forecasting, safetensors, t5, forecasting, time-series-forecasting ## Links - Model: https://huggingface.co/amazon/chronos-2 - Files: https://huggingface.co/amazon/chronos-2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download amazon/chronos-2 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [amazon/chronos-2 on Hugging Face](https://huggingface.co/amazon/chronos-2) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Chronos-2 (this page) | 8,097,695 | 575 | 119M | apache-2.0 | 2026-06-05 | | [NuExtract3](https://mindket.com/p/d90cc768-8f35-406a-81d8-92530ea071d9) | 40,915 | 397 | 4.5B | apache-2.0 | 2026-08-20 | | [Laplace-1-Atlas](https://mindket.com/p/6546e7f2-1972-480e-9db7-e90bcc3c028a) | 10,692 | 51 | 27.4B | apache-2.0 | 2026-11-04 | | [Laplace-1-Vector by JuneoAI](https://mindket.com/p/582c147c-3f50-425a-9034-d702e913d09f) | 10,582 | 44 | 4.1B | apache-2.0 | 2026-11-10 | | [H2O-Lightning-4B](https://mindket.com/p/f65f5f72-06bb-4b2d-a616-49352aeaa08d) | 10,530 | 107 | 4.7B | apache-2.0 | 2026-11-01 | | [pplx-decider-v1.1-27b](https://mindket.com/p/f25f6ff8-5eac-4981-ba4e-4bb9366506da) | 4,065 | 182 | 26.1B | apache-2.0 | 2026-10-12 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · abdulfatir · 2025-10-22](https://news.ycombinator.com/item?id=45669417): Chronos-2: From univariate to universal forecasting - [Bluesky · @strike007.bsky.social · 2026-08-28](https://bsky.app/profile/strike007.bsky.social/post/3mu633ahigs2j): Google’s push to fix AI benchmarking integrity matters for enterprise ROI. Flawed metrics inflate vendor claims, leading to misallocated compute budgets and failed deployments. (1/2) - [Bluesky · Georg · 2026-10-05](https://bsky.app/profile/gwlthr.com/post/3mx47mwunea2l): This study evaluates Chronos-2, a multi-channel TSFM from Amazon that supports univariate, multivariate, and covariate-informed forecasting, on two real-world utility datasets: ISO New England and ENTSO-E. - [Bluesky · Sergio Cuellar · 2026-09-17](https://bsky.app/profile/sergiocuellar.bsky.social/post/3mvomtmurfw2f): "Decathlon scales accurate demand forecasting with Chronos-2 on AWS, reducing inventory costs and improving product availability. 📈💼 \#AI \#RetailTech \#DemandForecasting" - [Bluesky · ApisDom · 2026-08-29](https://bsky.app/profile/apisdom.bsky.social/post/3muaa6rztpk2m): Mas info y comparador de métodos de predicción, basado en estudios recientes, para ver cuándo puede encajar mejor Chronos-2, Moirai-2, XGBoost u otros enfoques. Ver Forecast y el comparador: foresight.apisdom.com/es/pages/app... - [Bluesky · NEP-FOR: Forecasting · 2026-01-05](https://bsky.app/profile/repec-nep-for.bsky.social/post/3mboimbb7x52a): Chronos-2: From Univariate to Universal Forecasting: Amazon Web Services ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-06-05 - First published: 2025-10-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open amazon/chronos-2 on Hugging Face](https://huggingface.co/amazon/chronos-2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f1e85444-10b6-4178-a76d-741bfe468261) - [Its page on Mindket](https://mindket.com/p/f1e85444-10b6-4178-a76d-741bfe468261)