{"id":17030193,"url":"https://github.com/shenxiangzhuang/pysesd","last_synced_at":"2025-07-06T08:34:17.779Z","repository":{"id":180335003,"uuid":"655612834","full_name":"shenxiangzhuang/pysesd","owner":"shenxiangzhuang","description":"Time Series Seasonal Extreme Studentized Deviate(S-ESD) in Python","archived":false,"fork":false,"pushed_at":"2024-10-21T23:02:53.000Z","size":1311,"stargazers_count":1,"open_issues_count":1,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-10-23T06:40:42.585Z","etag":null,"topics":["anomaly-detection","esd","extreme-value-statistics","python","s-h-esd","statistics","time-series","twitter"],"latest_commit_sha":null,"homepage":"https://datahonor.com/pysesd/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/shenxiangzhuang.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-06-19T08:49:17.000Z","updated_at":"2024-10-08T04:39:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"b96dd5c7-d7a0-4306-8a34-b8d37dd78a1e","html_url":"https://github.com/shenxiangzhuang/pysesd","commit_stats":null,"previous_names":["shenxiangzhuang/pysesd"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shenxiangzhuang%2Fpysesd","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shenxiangzhuang%2Fpysesd/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shenxiangzhuang%2Fpysesd/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/shenxiangzhuang%2Fpysesd/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/shenxiangzhuang","download_url":"https://codeload.github.com/shenxiangzhuang/pysesd/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":227096649,"owners_count":17730376,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anomaly-detection","esd","extreme-value-statistics","python","s-h-esd","statistics","time-series","twitter"],"created_at":"2024-10-14T08:04:58.543Z","updated_at":"2024-11-29T10:11:57.367Z","avatar_url":"https://github.com/shenxiangzhuang.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cp align=\"center\" style=\"font-size:40px; margin:0px 10px 0px 10px\"\u003e\n    \u003cem\u003e[Py]S-ESD\u003c/em\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n    \u003cem\u003eSeasonal Extreme Studentized Deviate(S-ESD) in Python.\n\u003c/em\u003e\n\u003c/p\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://github.com/shenxiangzhuang/pysesd/actions/workflows/test.yaml\" target=\"_blank\"\u003e\n      \u003cimg src=\"https://github.com/shenxiangzhuang/pysesd/actions/workflows/test.yaml/badge.svg?event=pull_request\" alt=\"Test\"\u003e\n  \u003c/a\u003e\n\n  \u003ca href=\"https://github.com/shenxiangzhuang/pysesd\"\u003e\n  \u003cimg alt=\"Documentation\" src=\"https://github.com/shenxiangzhuang/pysesd/actions/workflows/build_docs.yaml/badge.svg\"/\u003e\n  \u003c/a\u003e\n\n  \u003ca href=\"#\"\u003e\n  \u003cimg src=\"https://img.shields.io/badge/Python-3.8, 3.9, 3.10, 3.11-blue\"\u003e\n  \u003c/a\u003e\n\n  \u003ca href=\"https://pypi.org/project/pysesd\" target=\"_blank\"\u003e\n      \u003cimg src=\"https://badge.fury.io/py/pysesd.svg\" alt=\"PyPI Package\"\u003e\n  \u003c/a\u003e\n  \u003ca href=\"https://codecov.io/gh/shenxiangzhuang/pysesd\" target=\"_blank\"\u003e\n      \u003cimg src=\"https://codecov.io/gh/shenxiangzhuang/pysesd/branch/master/graph/badge.svg\" alt=\"Coverage\"\u003e\n  \u003c/a\u003e\n\n\u003c/div\u003e\n\n# About\nSeasonal Extreme Studentized Deviate(S-ESD) in Python.\n\n# Installation\n\nGet latest version from PyPI:\n\n```shell\npip install pysesd\n```\n\n# Getting started\n\n## Simple example\n```python\nfrom pysesd.dataset import load_synthetic_ts\nfrom pysesd.sesd import SESD\n\nts = load_synthetic_ts()\nsesd = SESD(alpha=0.05, hybrid=False, max_outliers=2)\noutliers = sesd.fit(ts)\nsesd.plot(save=True, fig_dir=\"../figures\", fig_name=\"simple.png\")\n```\n![](./figures/simple.png)\n\n## Twitter example\n```python\nfrom pysesd.dataset import load_twitter_ts\nfrom pysesd.sesd import SESD\n\nts = load_twitter_ts()\nsesd = SESD(alpha=0.05, hybrid=True, max_outliers=int(len(ts) * 0.02))\noutliers = sesd.fit(ts)\nsesd.plot(save=True, fig_dir=\"../figures\", fig_name=\"twitter.png\")\n```\n![](./figures/twitter.png)\n\n\n# References\n- [twitter/AnomalyDetection](https://github.com/twitter/AnomalyDetection)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshenxiangzhuang%2Fpysesd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshenxiangzhuang%2Fpysesd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshenxiangzhuang%2Fpysesd/lists"}