[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71115":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":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":18,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":21,"hasPages":23,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":25,"readmeContent":26,"aiSummary":27,"trendingCount":16,"starSnapshotCount":16,"syncStatus":17,"lastSyncTime":28,"discoverSource":29},71115,"Kats","facebookresearch\u002FKats","facebookresearch","Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends. ","",null,"Python",6306,620,84,59,0,2,6,39.38,"MIT License",false,"main",true,[],"2026-06-12 02:02:48","\u003Cdiv align=\"center\">\n\u003Cimg src=\"kats_logo.svg\" width=\"40%\"\u002F>\n\u003C\u002Fdiv>\n\n\u003Cdiv align=\"center\">\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats\u002Factions\">\n  \u003Cimg alt=\"Github Actions\" src=\"https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats\u002Factions\u002Fworkflows\u002Fbuild_and_test.yml\u002Fbadge.svg\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fkats\">\n  \u003Cimg alt=\"PyPI Version\" src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fkats.svg\"\u002F>\n  \u003C\u002Fa>\n  \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md\">\n  \u003Cimg alt=\"PRs Welcome\" src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FPRs-welcome-brightgreen.svg\"\u002F>\n  \u003C\u002Fa>\n\u003C\u002Fdiv>\n\n## Description\n\nKats is a toolkit to analyze time series data, a lightweight, easy-to-use, and generalizable framework to perform time series analysis. Time series analysis is an essential component of Data Science and Engineering work at industry, from understanding the key statistics and characteristics, detecting regressions and anomalies, to forecasting future trends. Kats aims to provide the one-stop shop for time series analysis, including detection, forecasting, feature extraction\u002Fembedding, multivariate analysis, etc.\n\nKats is released by Facebook's *Infrastructure Data Science* team. It is available for download on [PyPI](https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fkats\u002F).\n\n## Important links\n\n- Homepage: https:\u002F\u002Ffacebookresearch.github.io\u002FKats\u002F\n- Kats Python package: https:\u002F\u002Fpypi.org\u002Fproject\u002Fkats\u002F\n- Facebook Engineering Blog Post: https:\u002F\u002Fengineering.fb.com\u002F2021\u002F06\u002F21\u002Fopen-source\u002Fkats\u002F\n- Source code repository: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002Fkats\n- Contributing: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats\u002Fblob\u002Fmaster\u002FCONTRIBUTING.md\n- Tutorials: https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats\u002Ftree\u002Fmaster\u002Ftutorials\n\n## Installation in Python\n\nKats is on PyPI, so you can use `pip` to install it.\n\n```bash\npip install --upgrade pip\npip install kats\n```\n\nIf you need only a small subset of Kats, you can install a minimal version of Kats with\n```bash\nMINIMAL_KATS=1 pip install kats\n```\nwhich omits many dependencies (everything in `test_requirements.txt`).\nHowever, this will disable many functionalities and cause `import kats` to log\nwarnings. See `setup.py` for full details and options.\n\n## Examples\n\nHere are a few sample snippets from a subset of Kats offerings:\n\n### Forecasting\n\nUsing `Prophet` model to forecast the `air_passengers` data set.\n\n```python\nimport pandas as pd\n\nfrom kats.consts import TimeSeriesData\nfrom kats.models.prophet import ProphetModel, ProphetParams\n\n# take `air_passengers` data as an example\nair_passengers_df = pd.read_csv(\n    \"..\u002Fkats\u002Fdata\u002Fair_passengers.csv\",\n    header=0,\n    names=[\"time\", \"passengers\"],\n)\n\n# convert to TimeSeriesData object\nair_passengers_ts = TimeSeriesData(air_passengers_df)\n\n# create a model param instance\nparams = ProphetParams(seasonality_mode='multiplicative') # additive mode gives worse results\n\n# create a prophet model instance\nm = ProphetModel(air_passengers_ts, params)\n\n# fit model simply by calling m.fit()\nm.fit()\n\n# make prediction for next 30 month\nfcst = m.predict(steps=30, freq=\"MS\")\n```\n\n### Detection\n\nUsing `CUSUM` detection algorithm on simulated data set.\n\n```python\n# import packages\nimport numpy as np\nimport pandas as pd\n\nfrom kats.consts import TimeSeriesData\nfrom kats.detectors.cusum_detection import CUSUMDetector\n\n# simulate time series with increase\nnp.random.seed(10)\ndf_increase = pd.DataFrame(\n    {\n        'time': pd.date_range('2019-01-01', '2019-03-01'),\n        'increase':np.concatenate([np.random.normal(1,0.2,30), np.random.normal(2,0.2,30)]),\n    }\n)\n\n# convert to TimeSeriesData object\ntimeseries = TimeSeriesData(df_increase)\n\n# run detector and find change points\nchange_points = CUSUMDetector(timeseries).detector()\n```\n\n### TSFeatures\n\nWe can extract meaningful features from the given time series data\n\n```python\n# Initiate feature extraction class\nimport pandas as pd\nfrom kats.consts import TimeSeriesData\nfrom kats.tsfeatures.tsfeatures import TsFeatures\n\n# take `air_passengers` data as an example\nair_passengers_df = pd.read_csv(\n    \"..\u002Fkats\u002Fdata\u002Fair_passengers.csv\",\n    header=0,\n    names=[\"time\", \"passengers\"],\n)\n\n# convert to TimeSeriesData object\nair_passengers_ts = TimeSeriesData(air_passengers_df)\n\n# calculate the TsFeatures\nfeatures = TsFeatures().transform(air_passengers_ts)\n```\n\n## Citing Kats\n\nIf you use Kats in your work or research, please use the following BibTeX entry.\n\n```\n@software{Jiang_KATS_2022,\nauthor = {Jiang, Xiaodong and Srivastava, Sudeep and Chatterjee, Sourav and Yu, Yang and Handler, Jeffrey and Zhang, Peiyi and Bopardikar, Rohan and Li, Dawei and Lin, Yanjun and Thakore, Uttam and Brundage, Michael and Holt, Ginger and Komurlu, Caner and Nagalla, Rakshita and Wang, Zhichao and Sun, Hechao and Gao, Peng and Cheung, Wei and Gao, Jun and Wang, Qi and Guerard, Marius and Kazemi, Morteza and Chen, Yulin and Zhou, Chong and Lee, Sean and Laptev, Nikolay and Levendovszky, Tihamér and Taylor, Jake and Qian, Huijun and Zhang, Jian and Shoydokova, Aida and Singh, Trisha and Zhu, Chengjun and Baz, Zeynep and Bergmeir, Christoph and Yu, Di and Koylan, Ahmet and Jiang, Kun and Temiyasathit, Ploy and Yurtbay, Emre},\nlicense = {MIT License},\nmonth = {3},\ntitle = {{Kats}},\nurl = {https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats},\nversion = {0.2.0},\nyear = {2022}\n}\n```\n\n## Core Algorithm and Related Papers\n\nCore Algorithm\n* Self-supervised learning for fast and scalable time series hyper-parameter tuning\n\nA self-supervised framework designed to automatically optimize hyper-parameters for time-series forecasting models at scale. The method reduces tuning cost, improves generalization across heterogeneous datasets, and supports efficient deployment in large production forecasting environments. The accompanying research was presented as an invited talk at international forecasting venues and has been referenced in work on automated model selection and adaptive time-series learning.\n\nMeta AI Official post: [Large-scale forecasting: Self-supervised learning framework for hyperparameter tuning](https:\u002F\u002Fai.meta.com\u002Fblog\u002Flarge-scale-forecasting-self-supervised-learning-framework-for-hyper-parameter-tuning\u002F)\n\nPaper: [Self-supervised Learning for Fast and Scalable Time Series Hyper-parameter Tuning](https:\u002F\u002Farxiv.org\u002Fabs\u002F2102.05740)\n\nInvited talk: [the 40th International Symposium on Forecasting](https:\u002F\u002Fisf.forecasters.org\u002Fwp-content\u002Fuploads\u002FISF20_program.pdf).\n\nIf you use this algorithm in your work or research, please use the following BibTeX entry.\n```\n@article{Zhang_SSL-HPT_2021,\n  author = {Zhang, Peiyi and Jiang, Xiaodong and Holt, Ginger M. and Laptev, Nikolay Pavlovich and Komurlu, Caner and Gao, Peng and Yu, Yang},\n  title  = {Self-supervised Learning for Fast and Scalable Time Series Hyper-parameter Tuning},\n  year   = {2021},\n  eprint = {2102.05740},\n  archivePrefix = {arXiv},\n  primaryClass = {cs.LG},\n  url    = {https:\u002F\u002Farxiv.org\u002Fpdf\u002F2102.05740}\n}\n```\n\n## Changelog\n\n### Version 0.2.0\n* Forecasting\n    * Added global model, a neural network forecasting model\n    * Added [global model tutorial](https:\u002F\u002Fgithub.com\u002Ffacebookresearch\u002FKats\u002Fblob\u002Fmain\u002Ftutorials\u002Fkats_205_globalmodel.ipynb)\n    * Consolidated backtesting APIs and some minor bug fixes\n* Detection\n    * Added model optimizer for anomaly\u002F changepoint detection\n    * Added evaluators for anomaly\u002Fchangepoint detection\n    * Improved simulators, to build synthetic data and inject anomalies\n    * Added new detectors: ProphetTrendDetector, Dynamic Time Warping based detectors\n    * Support for meta-learning, to recommend anomaly detection algorithms and parameters for your dataset\n    * Standardized API for some of our legacy detectors: OutlierDetector, MKDetector\n    * Support for Seasonality Removal in StatSigDetector\n* TsFeatures\n    * Added time-based features\n* Others\n    * Bug fixes, code coverage improvement, etc.\n\n### Version 0.1.0\n\n* Initial release\n\n## Contributors\nKats is currentely maintaned by community with the main contributions and leading from [Nickolai Kniazev](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnickknyazev\u002F) and Peter Shaffery\n\nKats is a project with several skillful researchers and engineers contributing to it.\nKats was started and built by [Xiaodong Jiang](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fxdjiang\u002F) with major contributions coming\nfrom many talented individuals in various forms and means. A non-exhaustive but growing list needs to mention: [Sudeep Srivastava](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsudeep-srivastava-2129484\u002F), [Sourav Chatterjee](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsouravc83\u002F), [Jeff Handler](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjeffhandl\u002F), [Rohan Bopardikar](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Frohan-bopardikar-30a99638), [Dawei Li](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Flidawei\u002F), [Yanjun Lin](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fyanjun-lin\u002F), [Yang Yu](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fyangyu2720\u002F), [Michael Brundage](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fmichaelb), [Caner Komurlu](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fckomurlu\u002F), [Rakshita Nagalla](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Frakshita-nagalla\u002F), [Zhichao Wang](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fzhichaowang\u002F), [Hechao Sun](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fhechao-sun-83b9ba4b\u002F), [Peng Gao](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fpeng-gao-9137a24b\u002F), [Wei Cheung](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fweizhicheung\u002F), [Jun Gao](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjun-gao-71352b64\u002F), [Qi Wang](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fqi-wang-9231a783\u002F), [Morteza Kazemi](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fmorteza-kazemi-pmp-csm\u002F), [Tihamér Levendovszky](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Ftiham%C3%A9r-levendovszky-29639b5\u002F), [Jian Zhang](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fjian-zhang-73718917\u002F), [Ahmet Koylan](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fahmetburhan\u002F), [Kun Jiang](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fkunqiang-jiang-ph-d-0988aa1b\u002F), [Aida Shoydokova](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fashoydok\u002F), [Ploy Temiyasathit](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fnutcha-temiyasathit\u002F), Sean Lee, [Nikolay Pavlovich Laptev](http:\u002F\u002Fwww.nikolaylaptev.com\u002F), [Peiyi Zhang](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fpyzhang\u002F), [Emre Yurtbay](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Femre-yurtbay-27516313a\u002F), [Daniel Dequech](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fdaniel-dequech\u002F), [Rui Yan](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Frui-yan\u002F), [William Luo](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fwqcluo\u002F), [Marius Guerard](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fmariusguerard\u002F), [Pietari Pulkkinen](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fpietaripulkkinen\u002F), [Uttam Thakore](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Futtam-thakore\u002F), [Trisha Singh](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Ftrishasingh2696\u002F), [Huijun Qian](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fhuijun-qian-0845a958\u002F), [Chengjun Zhu](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fchengjunzhu\u002F), [Di Yu](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fdiyu05\u002F), [Zeynep Erkin Baz](https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fzeynep-erkin-baz-51198a21), and [Christoph Bergmeir](https:\u002F\u002Fau.linkedin.com\u002Fin\u002Fchristoph-bergmeir-00711a14\u002F).\n\n## License\n\nKats is licensed under the [MIT license](LICENSE).\n","Kats 是一个用于时间序列数据分析的工具包，提供了一个轻量级、易用且可扩展的框架。其核心功能包括关键统计与特征的理解、变化点和异常检测以及未来趋势预测。Kats 采用了Python语言编写，支持多种时间序列分析任务，如检测、预测、特征提取\u002F嵌入及多变量分析等。适用于需要进行时间序列数据分析的各种场景，尤其是在数据科学和工程领域中，能够帮助用户快速理解和处理时间序列数据。","2026-06-11 03:35:57","high_star"]