[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9789":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":18,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":22,"hasPages":22,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":40,"readmeContent":41,"aiSummary":42,"trendingCount":16,"starSnapshotCount":16,"syncStatus":43,"lastSyncTime":44,"discoverSource":45},9789,"gluonts","awslabs\u002Fgluonts","awslabs","Probabilistic time series modeling in Python","https:\u002F\u002Fts.gluon.ai",null,"Python",5199,819,75,345,0,1,3,18,70.04,"Apache License 2.0",false,"dev",[25,26,27,28,29,30,31,32,33,34,35,36,37,38,39],"artificial-intelligence","aws","data-science","deep-learning","forecasting","machine-learning","mxnet","neural-networks","pytorch","sagemaker","time-series","time-series-forecasting","time-series-prediction","timeseries","torch","2026-06-12 04:00:46","\u003Cimg class=\"hide-on-website\" height=\"100px\" src=\"https:\u002F\u002Fts.gluon.ai\u002Fdev\u002F_static\u002Fgluonts.svg\">\n\n# GluonTS - Probabilistic Time Series Modeling in Python\n\n[![PyPI](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fgluonts.svg?style=flat-square&color=b75347)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fgluonts\u002F)\n[![GitHub](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fawslabs\u002Fgluonts.svg?style=flat-square&color=df7e66)](.\u002FLICENSE)\n[![Static](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=docs&message=stable&color=edc775&style=flat-square)](https:\u002F\u002Fts.gluon.ai\u002F)\n[![Static](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?label=docs&message=dev&color=edc775&style=flat-square)](https:\u002F\u002Fts.gluon.ai\u002Fdev\u002F)\n[![PyPI Downloads](https:\u002F\u002Fstatic.pepy.tech\u002Fbadge\u002Fgluonts\u002Fmonth)](https:\u002F\u002Fpepy.tech\u002Fprojects\u002Fgluonts)\n\n**📢 BREAKING NEWS**: We released **Chronos**, a suite of pretrained models for zero-shot time series forecasting. Chronos can generate accurate probabilistic predictions for new time series not seen during training. Check it out [here](https:\u002F\u002Fgithub.com\u002Famazon-science\u002Fchronos-forecasting)!\n\nGluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models,\nbased on [PyTorch](https:\u002F\u002Fpytorch.org) and [MXNet](https:\u002F\u002Fmxnet.apache.org).\n\n\n## Installation\n\nGluonTS requires Python 3.7 or newer, and the easiest way to install it is via\n`pip`:\n\n```bash\n# install with support for torch models\npip install \"gluonts[torch]\"\n\n# install with support for mxnet models\npip install \"gluonts[mxnet]\"\n```\n\nSee the [documentation](https:\u002F\u002Fts.gluon.ai\u002Fstable\u002Fgetting_started\u002Finstall.html)\nfor more info on how GluonTS can be installed.\n\n## Simple Example\n\nTo illustrate how to use GluonTS, we train a DeepAR-model and make predictions\nusing the airpassengers dataset. The dataset consists of a single time\nseries of monthly passenger numbers between 1949 and 1960. We train the model\non the first nine years and make predictions for the remaining three years.\n\n```py\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom gluonts.dataset.pandas import PandasDataset\nfrom gluonts.dataset.split import split\nfrom gluonts.torch import DeepAREstimator\n\n# Load data from a CSV file into a PandasDataset\ndf = pd.read_csv(\n    \"https:\u002F\u002Fraw.githubusercontent.com\u002FAileenNielsen\u002F\"\n    \"TimeSeriesAnalysisWithPython\u002Fmaster\u002Fdata\u002FAirPassengers.csv\",\n    index_col=0,\n    parse_dates=True,\n)\ndataset = PandasDataset(df, target=\"#Passengers\")\n\n# Split the data for training and testing\ntraining_data, test_gen = split(dataset, offset=-36)\ntest_data = test_gen.generate_instances(prediction_length=12, windows=3)\n\n# Train the model and make predictions\nmodel = DeepAREstimator(\n    prediction_length=12, freq=\"M\", trainer_kwargs={\"max_epochs\": 5}\n).train(training_data)\n\nforecasts = list(model.predict(test_data.input))\n\n# Plot predictions\nplt.plot(df[\"1954\":], color=\"black\")\nfor forecast in forecasts:\n  forecast.plot()\nplt.legend([\"True values\"], loc=\"upper left\", fontsize=\"xx-large\")\nplt.show()\n```\n\n![[train-test]](https:\u002F\u002Fts.gluon.ai\u002Fstatic\u002FREADME\u002Fforecasts.png)\n\nNote, the forecasts are displayed in terms of a probability distribution and\nthe shaded areas represent the 50% and 90% prediction intervals.\n\n\n## Contributing\n\nIf you wish to contribute to the project, please refer to our\n[contribution guidelines](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fgluonts\u002Ftree\u002Fdev\u002FCONTRIBUTING.md).\n\n## Citing\n\nIf you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers,\nin addition to any model-specific references that are relevant for your work:\n\n```bibtex\n@article{gluonts_jmlr,\n  author  = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider\n    and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix\n    and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and\n    Ali Caner Türkmen and Yuyang Wang},\n  title   = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}},\n  journal = {Journal of Machine Learning Research},\n  year    = {2020},\n  volume  = {21},\n  number  = {116},\n  pages   = {1-6},\n  url     = {http:\u002F\u002Fjmlr.org\u002Fpapers\u002Fv21\u002F19-820.html}\n}\n```\n\n```bibtex\n@article{gluonts_arxiv,\n  author  = {Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. and\n    Flunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C.\n    and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. and\n    Türkmen, A. C. and Wang, Y.},\n  title   = {{GluonTS: Probabilistic Time Series Modeling in Python}},\n  journal = {arXiv preprint arXiv:1906.05264},\n  year    = {2019}\n}\n```\n\n## Links\n\n### Documentation\n\n* [Documentation (stable)](https:\u002F\u002Fts.gluon.ai\u002Fstable\u002F)\n* [Documentation (development)](https:\u002F\u002Fts.gluon.ai\u002Fdev\u002F)\n\n### References\n\n* [JMLR MLOSS Paper](http:\u002F\u002Fwww.jmlr.org\u002Fpapers\u002Fv21\u002F19-820.html)\n* [ArXiv Paper](https:\u002F\u002Farxiv.org\u002Fabs\u002F1906.05264)\n* [Collected Papers from the group behind GluonTS](https:\u002F\u002Fgithub.com\u002Fawslabs\u002Fgluonts\u002Ftree\u002Fdev\u002FREFERENCES.md): a bibliography.\n\n### Tutorials and Workshops\n\n* [Tutorial at IJCAI 2021 (with videos)](https:\u002F\u002Flovvge.github.io\u002FForecasting-Tutorial-IJCAI-2021\u002F) with [YouTube link](https:\u002F\u002Fyoutu.be\u002FAB3I9pdT46c). \n* [Tutorial at WWW 2020 (with videos)](https:\u002F\u002Flovvge.github.io\u002FForecasting-Tutorial-WWW-2020\u002F)\n* [Tutorial at SIGMOD 2019](https:\u002F\u002Flovvge.github.io\u002FForecasting-Tutorials\u002FSIGMOD-2019\u002F)\n* [Tutorial at KDD 2019](https:\u002F\u002Flovvge.github.io\u002FForecasting-Tutorial-KDD-2019\u002F)\n* [Tutorial at VLDB 2018](https:\u002F\u002Flovvge.github.io\u002FForecasting-Tutorial-VLDB-2018\u002F)\n* [Neural Time Series with GluonTS](https:\u002F\u002Fyoutu.be\u002FbeEJMIt9xJ8)\n* [International Symposium of Forecasting: Deep Learning for Forecasting workshop](https:\u002F\u002Flostella.github.io\u002FISF-2020-Deep-Learning-Workshop\u002F)\n","GluonTS 是一个用于概率时间序列建模的 Python 库，特别强调基于深度学习的方法。它支持 PyTorch 和 MXNet 框架，提供了多种先进的模型如 DeepAR，能够生成准确的时间序列预测，并且具备处理不确定性估计的能力。该库适用于需要进行时间序列分析和预测的各种场景，比如金融市场的趋势预测、电力需求预测等。其简洁易用的 API 使得用户可以快速上手并构建复杂的时间序列模型。",2,"2026-06-11 03:24:46","top_topic"]