[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9786":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":39,"readmeContent":40,"aiSummary":41,"trendingCount":16,"starSnapshotCount":16,"syncStatus":42,"lastSyncTime":43,"discoverSource":44},9786,"chronos-forecasting","amazon-science\u002Fchronos-forecasting","amazon-science","Chronos: Pretrained Models for Time Series Forecasting","https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.15821",null,"Python",5450,648,43,23,0,5,35,164,28,39.44,"Apache License 2.0",false,"main",[26,27,28,29,30,31,32,33,34,35,36,37,38],"artificial-intelligence","forecasting","foundation-models","huggingface","huggingface-transformers","large-language-models","llm","machine-learning","pretrained-models","time-series","time-series-forecasting","timeseries","transformers","2026-06-12 02:02:12","\u003Cdiv align=\"center\">\n\n# Chronos: Pretrained Models for Time Series Forecasting\n\n[![preprint](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=Chronos-Paper&message=2403.07815&color=B31B1B&logo=arXiv)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.07815)\n[![preprint](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=Chronos-2-Report&message=2510.15821&color=B31B1B&logo=arXiv)](https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.15821)\n[![huggingface](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20HF-Datasets-FFD21E)](https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002Fautogluon\u002Fchronos_datasets)\n[![huggingface](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20HF-Models-FFD21E)](https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Famazon\u002Fchronos-models-65f1791d630a8d57cb718444)\n[![fev](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=fev&message=Benchmark&color=B31B1B&logo=github)](https:\u002F\u002Fgithub.com\u002Fautogluon\u002Ffev)\n[![aws](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=SageMaker&message=Deploy&color=FF9900&logo=amazon-web-services)](notebooks\u002Fdeploy-chronos-to-amazon-sagemaker.ipynb)\n[![faq](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFAQ-Questions%3F-blue)](https:\u002F\u002Fgithub.com\u002Famazon-science\u002Fchronos-forecasting\u002Fissues?q=is%3Aissue+label%3AFAQ)\n[![License: MIT](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-Apache--2.0-green.svg)](https:\u002F\u002Fopensource.org\u002Flicenses\u002FApache-2.0)\n\n\u003C\u002Fdiv>\n\n\n## 🚀 News\n- **30 Dec 2025**: ☁️ Deploy Chronos-2 to AWS with Amazon SageMaker: new guide covers real-time inference (GPU\u002FCPU), serverless endpoints with automatic scaling, and batch transform for large-scale forecasting. See the [deployment tutorial](notebooks\u002Fdeploy-chronos-to-amazon-sagemaker.ipynb).\n- **20 Oct 2025**: 🚀 [Chronos-2](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-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](notebooks\u002Fchronos-2-quickstart.ipynb) to get started with Chronos-2.\n- **12 Dec 2024**: 📊 We released [`fev`](https:\u002F\u002Fgithub.com\u002Fautogluon\u002Ffev), a lightweight package for benchmarking time series forecasting models based on the [Hugging Face `datasets`](https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fdatasets\u002Fen\u002Findex) library.\n- **26 Nov 2024**: ⚡️ Chronos-Bolt models released [on HuggingFace](https:\u002F\u002Fhuggingface.co\u002Fcollections\u002Famazon\u002Fchronos-models-65f1791d630a8d57cb718444). 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!\n- **13 Mar 2024**: 🚀 Chronos [paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F2403.07815) and inference code released.\n\n## ✨ Introduction\n\nThis package provides an interface to the Chronos family of **pretrained time series forecasting models**. The following model types are supported.\n\n- **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](https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.15821).\n- **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](https:\u002F\u002Faws.amazon.com\u002Fblogs\u002Fmachine-learning\u002Ffast-and-accurate-zero-shot-forecasting-with-chronos-bolt-and-autogluon\u002F).\n- **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](https:\u002F\u002Fopenreview.net\u002Fforum?id=gerNCVqqtR).\n\n### Available Models\n\n\u003Cdiv align=\"center\">\n\n| Model ID                                                               | Parameters |\n| ---------------------------------------------------------------------- | ---------- |\n| [`amazon\u002Fchronos-2`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-2)   | 120M         |\n| [`autogluon\u002Fchronos-2-synth`](https:\u002F\u002Fhuggingface.co\u002Fautogluon\u002Fchronos-2-synth)   | 120M         |\n| [`autogluon\u002Fchronos-2-small`](https:\u002F\u002Fhuggingface.co\u002Fautogluon\u002Fchronos-2-small)   | 28M         |\n| [`amazon\u002Fchronos-bolt-tiny`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-bolt-tiny)   | 9M         |\n| [`amazon\u002Fchronos-bolt-mini`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-bolt-mini)   | 21M        |\n| [`amazon\u002Fchronos-bolt-small`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-bolt-small) | 48M        |\n| [`amazon\u002Fchronos-bolt-base`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-bolt-base)   | 205M       |\n| [`amazon\u002Fchronos-t5-tiny`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-t5-tiny)   | 8M         |\n| [`amazon\u002Fchronos-t5-mini`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-t5-mini)   | 20M        |\n| [`amazon\u002Fchronos-t5-small`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-t5-small) | 46M        |\n| [`amazon\u002Fchronos-t5-base`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-t5-base)   | 200M       |\n| [`amazon\u002Fchronos-t5-large`](https:\u002F\u002Fhuggingface.co\u002Famazon\u002Fchronos-t5-large) | 710M       |\n\n\u003C\u002Fdiv>\n\n## 📈 Usage\n\nTo perform inference with Chronos, the easiest way is to install this package through `pip`:\n\n```sh\npip install chronos-forecasting\n```\n\n> [!TIP]\n> For reliable production use, we recommend using Chronos-2 models through [Amazon SageMaker JumpStart](https:\u002F\u002Faws.amazon.com\u002Fsagemaker\u002Fai\u002Fjumpstart\u002F). Check out [this tutorial](notebooks\u002Fdeploy-chronos-to-amazon-sagemaker.ipynb) to learn how to deploy Chronos-2 inference endpoints to AWS with just a few lines of code.\n\n\n### Forecasting\n\nA minimal example showing how to perform forecasting using Chronos-2:\n\n```python\nimport pandas as pd  # requires: pip install 'pandas[pyarrow]'\nfrom chronos import Chronos2Pipeline\n\npipeline = Chronos2Pipeline.from_pretrained(\"amazon\u002Fchronos-2\", device_map=\"cuda\")\n\n# Load historical target values and past values of covariates\ncontext_df = pd.read_parquet(\"https:\u002F\u002Fautogluon.s3.amazonaws.com\u002Fdatasets\u002Ftimeseries\u002Felectricity_price\u002Ftrain.parquet\")\n\n# (Optional) Load future values of covariates\ntest_df = pd.read_parquet(\"https:\u002F\u002Fautogluon.s3.amazonaws.com\u002Fdatasets\u002Ftimeseries\u002Felectricity_price\u002Ftest.parquet\")\nfuture_df = test_df.drop(columns=\"target\")\n\n# Generate predictions with covariates\npred_df = pipeline.predict_df(\n    context_df,\n    future_df=future_df,\n    prediction_length=24,  # Number of steps to forecast\n    quantile_levels=[0.1, 0.5, 0.9],  # Quantile for probabilistic forecast\n    id_column=\"id\",  # Column identifying different time series\n    timestamp_column=\"timestamp\",  # Column with datetime information\n    target=\"target\",  # Column(s) with time series values to predict\n)\n```\n\nWe can now visualize the forecast:\n\n```python\nimport matplotlib.pyplot as plt  # requires: pip install matplotlib\n\nts_context = context_df.set_index(\"timestamp\")[\"target\"].tail(256)\nts_pred = pred_df.set_index(\"timestamp\")\nts_ground_truth = test_df.set_index(\"timestamp\")[\"target\"]\n\nts_context.plot(label=\"historical data\", color=\"xkcd:azure\", figsize=(12, 3))\nts_ground_truth.plot(label=\"future data (ground truth)\", color=\"xkcd:grass green\")\nts_pred[\"predictions\"].plot(label=\"forecast\", color=\"xkcd:violet\")\nplt.fill_between(\n    ts_pred.index,\n    ts_pred[\"0.1\"],\n    ts_pred[\"0.9\"],\n    alpha=0.7,\n    label=\"prediction interval\",\n    color=\"xkcd:light lavender\",\n)\nplt.legend()\n```\n\n## Example Notebooks\n\n- [Chronos-2 Quick Start](notebooks\u002Fchronos-2-quickstart.ipynb)\n   \n  \u003Ca href=\"https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fimport\u002Fgithub\u002Famazon-science\u002Fchronos-forecasting\u002Fblob\u002Fmain\u002Fnotebooks\u002Fchronos-2-quickstart.ipynb\">\n    \u003Cimg src=\"https:\u002F\u002Fstudiolab.sagemaker.aws\u002Fstudiolab.svg\" alt=\"Open In SageMaker Studio Lab\" height=\"18\" align=\"absmiddle\">\n  \u003C\u002Fa>\n   \n  \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Famazon-science\u002Fchronos-forecasting\u002Fblob\u002Fmain\u002Fnotebooks\u002Fchronos-2-quickstart.ipynb\">\n    \u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\" height=\"18\" align=\"absmiddle\">\n  \u003C\u002Fa>\n- [Deploy Chronos-2 on Amazon SageMaker](notebooks\u002Fdeploy-chronos-to-amazon-sagemaker.ipynb)\n\n## 📝 Citation\n\nIf you find Chronos models useful for your research, please consider citing the associated papers:\n\n```\n@article{ansari2024chronos,\n  title={Chronos: Learning the Language of Time Series},\n  author={Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang},\n  journal={Transactions on Machine Learning Research},\n  issn={2835-8856},\n  year={2024},\n  url={https:\u002F\u002Fopenreview.net\u002Fforum?id=gerNCVqqtR}\n}\n\n@article{ansari2025chronos2,\n  title        = {Chronos-2: From Univariate to Universal Forecasting},\n  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},\n  journal      = {arXiv preprint arXiv:2510.15821},\n  year         = {2025},\n  url          = {https:\u002F\u002Farxiv.org\u002Fabs\u002F2510.15821}\n}\n```\n\n## 🛡️ Security\n\nSee [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information.\n\n## 📃 License\n\nThis project is licensed under the Apache-2.0 License.\n","Chronos 是一个用于时间序列预测的预训练模型库。它支持多种类型的预测任务，包括单变量、多变量以及基于协变量的信息预测。Chronos-2 作为最新版本，提供了零样本学习能力，能够在没有额外训练的情况下处理不同类型的预测任务，并且在多个基准测试中表现出色。该项目使用 Python 开发，基于 Hugging Face 的 Transformers 库构建，适用于需要高效准确的时间序列预测的应用场景，如金融分析、天气预报、需求预测等。通过 Amazon SageMaker 可以轻松部署这些模型进行实时或批量预测。",2,"2026-06-11 03:24:43","top_topic"]