[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-sktime--sktime":3,"tool-sktime--sktime":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":67,"owner_name":67,"owner_avatar_url":75,"owner_bio":68,"owner_company":76,"owner_location":76,"owner_email":77,"owner_twitter":76,"owner_website":78,"owner_url":79,"languages":80,"stars":106,"forks":107,"last_commit_at":108,"license":109,"difficulty_score":110,"env_os":111,"env_gpu":112,"env_ram":112,"env_deps":113,"category_tags":126,"github_topics":127,"view_count":141,"oss_zip_url":76,"oss_zip_packed_at":76,"status":16,"created_at":142,"updated_at":143,"faqs":144,"releases":172},1188,"sktime\u002Fsktime","sktime","A unified framework for machine learning with time series","sktime 是一个用于时间序列分析的 Python 库，提供统一的接口来处理多种时间序列机器学习任务，如预测、分类、聚类、异常检测等。它简化了时间序列数据的建模流程，让开发者能够更高效地构建、调优和验证模型。sktime 与 scikit-learn 兼容，适合希望在时间序列上应用机器学习技术的开发者和研究人员。其设计注重易用性和灵活性，支持从基础到高级的多种应用场景，是处理时间序列问题的强大工具。","\n## Welcome to sktime\n\n\u003Ca href=\"https:\u002F\u002Fwww.sktime.net\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fdocs\u002Fsource\u002Fimages\u002Fsktime-logo.svg\" width=\"175\" align=\"right\" \u002F>\u003C\u002Fa>\n\n> A unified interface for machine learning with time series\n\n:rocket: **Version 0.40.1 out now!** [Check out the release notes here](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html).\n\nsktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly\u002Fchangepoint detection, and other tasks. It comes with [time series algorithms](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Festimator_overview.html) and [scikit-learn] compatible tools to build, tune, and validate time series models.\n\n[scikit-learn]: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002F\n\n|  | **[Documentation](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Fusers.html)** · **[Tutorials](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Fexamples.html)** · **[Release Notes](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Fchangelog.html)** |\n|---|---|\n| **Open Source** | [![BSD 3-clause](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-BSD%203--Clause-blue.svg)](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002FLICENSE) [![GC.OS Sponsored](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGC.OS-Sponsored%20Project-orange.svg?style=flat&colorA=0eac92&colorB=2077b4)](https:\u002F\u002Fgc-os-ai.github.io\u002F) |\n| **Tutorials** | [![Binder](https:\u002F\u002Fmybinder.org\u002Fbadge_logo.svg)](https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fsktime\u002Fsktime\u002Fmain?filepath=examples) [![!youtube](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?logo=youtube&label=YouTube&message=tutorials&color=red)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |\n| **Community** | [![!discord](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?logo=discord&label=discord&message=chat&color=lightgreen)](https:\u002F\u002Fdiscord.com\u002Finvite\u002F54ACzaFsn7) [![!slack](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fscikit-time\u002F)  |\n| **CI\u002FCD** | [![github-actions](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fsktime\u002Fsktime\u002Fwheels.yml?logo=github)](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Factions\u002Fworkflows\u002Fwheels.yml) [![readthedocs](https:\u002F\u002Fimg.shields.io\u002Freadthedocs\u002Fsktime?logo=readthedocs)](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002F?badge=latest) [![platform](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fpn\u002Fconda-forge\u002Fsktime)](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime) |\n| **Code** |  [![!pypi](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fsktime?color=orange)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fsktime\u002F) [![!conda](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fsktime)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fsktime) [![!python-versions](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fsktime)](https:\u002F\u002Fwww.python.org\u002F) [![!black](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcode%20style-black-000000.svg)](https:\u002F\u002Fgithub.com\u002Fpsf\u002Fblack)  |\n| **Downloads** | ![PyPI - Downloads](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdw\u002Fsktime) ![PyPI - Downloads](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fsktime) [![Downloads](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsktime_sktime_readme_0f3ad8369eaa.png))](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fsktime) |\n| **Citation** | [![!zenodo](https:\u002F\u002Fzenodo.org\u002Fbadge\u002FDOI\u002F10.5281\u002Fzenodo.3749000.svg)](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.3749000) |\n\n## :books: Documentation\n\n| Documentation                        |                                                                |\n|--------------------------------------| -------------------------------------------------------------- |\n| :star: **[Tutorials]**               | New to sktime? Here's everything you need to know!              |\n| :clipboard: **[Binder Notebooks]**   | Example notebooks to play with in your browser.              |\n| :woman_technologist: **[Examples]**  | How to use sktime and its features.                             |\n| :scissors: **[Extension Templates]** | How to build your own estimator using sktime's API.            |\n| :control_knobs: **[API Reference]**  | The detailed reference for sktime's API.                        |\n| :tv: **[Video Tutorial]**            | Our video tutorial from 2021 PyData Global.      |\n| :hammer_and_wrench: **[Changelog]**  | Changes and version history.                                   |\n| :deciduous_tree: **[Roadmap]**       | sktime's software and community development plan.                                   |\n| :pencil: **[Related Software]**      | A list of related software. |\n\n[tutorials]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Ftutorials.html\n[binder notebooks]: https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fsktime\u002Fsktime\u002Fmain?filepath=examples\n[examples]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fexamples.html\n[video tutorial]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime-tutorial-pydata-global-2021\n[api reference]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference.html\n[changelog]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html\n[roadmap]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Froadmap.html\n[related software]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Frelated_software.html\n\n## :speech_balloon: Where to ask questions\n\nQuestions and feedback are extremely welcome! We strongly believe in the value of sharing help publicly, as it allows a wider audience to benefit from it.\n\n| Type                            | Platforms                               |\n| ------------------------------- | --------------------------------------- |\n| :bug: **Bug Reports**              | [GitHub Issue Tracker]                  |\n| :sparkles: **Feature Requests & Ideas** | [GitHub Issue Tracker]                       |\n| :woman_technologist: **Usage Questions**          | [GitHub Discussions] · [Stack Overflow] |\n| :speech_balloon: **General Discussion**        | [GitHub Discussions] |\n| :factory: **Contribution & Development** | `dev-chat` channel · [Discord] |\n| :globe_with_meridians: **Meet-ups and collaboration sessions** | [Discord] - Fridays 13 UTC, dev\u002Fmeet-ups channel |\n\n[github issue tracker]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fissues\n[github discussions]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fdiscussions\n[stack overflow]: https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fsktime\n[discord]: https:\u002F\u002Fdiscord.com\u002Finvite\u002F54ACzaFsn7\n\n## :dizzy: Features\nOur objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a __unified interface for distinct but related time series learning tasks__. It features [__dedicated time series algorithms__](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Festimator_overview.html) and __tools for composite model building__,  such as pipelining, ensembling, tuning, and reduction, empowering users to apply algorithms designed for one task to another.\n\nsktime also provides **interfaces to related libraries**, for example [scikit-learn], [statsmodels], [tsfresh], [PyOD], and [fbprophet], among others.\n\n[statsmodels]: https:\u002F\u002Fwww.statsmodels.org\u002Fstable\u002Findex.html\n[tsfresh]: https:\u002F\u002Ftsfresh.readthedocs.io\u002Fen\u002Flatest\u002F\n[pyod]: https:\u002F\u002Fpyod.readthedocs.io\u002Fen\u002Flatest\u002F\n[fbprophet]: https:\u002F\u002Ffacebook.github.io\u002Fprophet\u002F\n\n| Module | Status | Links |\n|---|---|---|\n| **[Forecasting]** | stable | [Tutorial](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fexamples\u002F01_forecasting.html) · [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fforecasting.html) · [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fforecasting.py)  |\n| **[Time Series Classification]** | stable | [Tutorial](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fexamples\u002F02_classification.ipynb) · [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fclassification.html) · [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fclassification.py) |\n| **[Time Series Regression]** | stable | [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fregression.html) |\n| **[Transformations]** | stable | [Tutorial](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fexamples\u002F03_transformers.ipynb) · [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Ftransformations.html) · [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Ftransformer.py)  |\n| **[Detection tasks]** | maturing | [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fdetection.py) |\n| **[Parameter fitting]** | maturing | [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fparam_est.html) · [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Ftransformer.py)  |\n| **[Time Series Clustering]** | maturing | [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fclustering.html) ·  [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fclustering.py) |\n| **[Time Series Distances\u002FKernels]** | maturing | [Tutorial](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fexamples\u002F03_transformers.ipynb) · [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fdists_kernels.html) · [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fdist_kern_panel.py) |\n| **[Time Series Alignment]** | experimental | [API Reference](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Falignment.html) · [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Falignment.py) |\n| **[Time Series Splitters]** | maturing | [Extension Template](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fsplit.py) | |\n| **[Distributions and simulation]** | experimental |  |\n\n[forecasting]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fforecasting\n[time series classification]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fclassification\n[time series regression]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fregression\n[time series clustering]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fclustering\n[detection tasks]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fdetection\n[time series distances\u002Fkernels]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fdists_kernels\n[time series alignment]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Falignment\n[transformations]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Ftransformations\n[distributions and simulation]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fproba\n[time series splitters]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fsplit\n[parameter fitting]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fparam_est\n\n\n## :hourglass_flowing_sand: Install sktime\nFor troubleshooting and detailed installation instructions, see the [documentation](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Finstallation.html).\n\n- **Operating system**: macOS X · Linux · Windows 8.1 or higher\n- **Python version**: Python 3.10, 3.11, 3.12, and 3.13 (only 64-bit)\n- **Package managers**: [pip] · [conda] (via `conda-forge`)\n\n[pip]: https:\u002F\u002Fpip.pypa.io\u002Fen\u002Fstable\u002F\n[conda]: https:\u002F\u002Fdocs.conda.io\u002Fen\u002Flatest\u002F\n\n### pip\nUsing pip, sktime releases are available as source packages and binary wheels.\nAvailable wheels are listed [here](https:\u002F\u002Fpypi.org\u002Fsimple\u002Fsktime\u002F).\n\n```bash\npip install sktime\n```\n\nor, with maximum dependencies,\n\n```bash\npip install sktime[all_extras]\n```\n\nFor curated sets of soft dependencies for specific learning tasks:\n\n```bash\npip install sktime[forecasting]  # for selected forecasting dependencies\npip install sktime[forecasting,transformations]  # forecasters and transformers\n```\n\nor similar. Valid sets are:\n\n* `forecasting`\n* `transformations`\n* `classification`\n* `regression`\n* `clustering`\n* `param_est`\n* `networks`\n* `detection`\n* `alignment`\n\nCave: in general, not all soft dependencies for a learning task are installed,\nonly a curated selection.\n\n### conda\nYou can also install sktime from `conda` via the `conda-forge` channel.\nThe feedstock including the build recipe and configuration is maintained\nin [this conda-forge repository](https:\u002F\u002Fgithub.com\u002Fconda-forge\u002Fsktime-feedstock).\n\n```bash\nconda install -c conda-forge sktime\n```\n\nor, with maximum dependencies,\n\n```bash\nconda install -c conda-forge sktime-all-extras\n```\n\n(as `conda` does not support dependency sets,\nflexible choice of soft dependencies is unavailable via `conda`)\n\n## :zap: Quickstart\n\n### Forecasting\n\n``` python\nfrom sktime.datasets import load_airline\nfrom sktime.forecasting.base import ForecastingHorizon\nfrom sktime.forecasting.theta import ThetaForecaster\nfrom sktime.split import temporal_train_test_split\nfrom sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n\ny = load_airline()\ny_train, y_test = temporal_train_test_split(y)\nfh = ForecastingHorizon(y_test.index, is_relative=False)\nforecaster = ThetaForecaster(sp=12)  # monthly seasonal periodicity\nforecaster.fit(y_train)\ny_pred = forecaster.predict(fh)\nmean_absolute_percentage_error(y_test, y_pred)\n>>> 0.08661467738190656\n```\n\n### Time Series Classification\n\n```python\nfrom sktime.classification.interval_based import TimeSeriesForestClassifier\nfrom sktime.datasets import load_arrow_head\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nX, y = load_arrow_head()\nX_train, X_test, y_train, y_test = train_test_split(X, y)\nclassifier = TimeSeriesForestClassifier()\nclassifier.fit(X_train, y_train)\ny_pred = classifier.predict(X_test)\naccuracy_score(y_test, y_pred)\n>>> 0.8679245283018868\n```\n\n## :wave: How to get involved\n\nThere are many ways to join the sktime community. We follow the [all-contributors](https:\u002F\u002Fgithub.com\u002Fall-contributors\u002Fall-contributors) specification: all kinds of contributions are welcome - not just code.\n\n| Documentation              |                                                                |\n| -------------------------- | --------------------------------------------------------------        |\n| :gift_heart: **[Contribute]**        | How to contribute to sktime.          |\n| :school_satchel:  **[Mentoring]** | New to open source? Apply to our mentoring program! |\n| :date: **[Meetings]** | Join our discussions, tutorials, workshops, and sprints! |\n| :woman_mechanic:  **[Developer Guides]**      | How to further develop sktime's code base.                             |\n| :construction: **[Enhancement Proposals]** | Design a new feature for sktime. |\n| :medal_sports: **[Contributors]** | A list of all contributors. |\n| :raising_hand: **[Roles]** | An overview of our core community roles. |\n| :money_with_wings: **[Donate]** | Fund sktime maintenance and development. |\n| :classical_building: **[Governance]** | How and by whom decisions are made in sktime's community.   |\n\n[contribute]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fget_involved\u002Fcontributing.html\n[donate]: https:\u002F\u002Fopencollective.com\u002Fsktime\n[extension templates]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fextension_templates\n[developer guides]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fdeveloper_guide.html\n[contributors]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002FCONTRIBUTORS.md\n[governance]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fget_involved\u002Fgovernance.html\n[mentoring]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fmentoring\n[meetings]: https:\u002F\u002Fcalendar.google.com\u002Fcalendar\u002Fu\u002F0\u002Fembed?src=sktime.toolbox@gmail.com&ctz=UTC\n[enhancement proposals]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fenhancement-proposals\n[roles]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fabout\u002Fteam.html\n\n## :trophy: Hall of fame\n\nThanks to all our community for all your wonderful contributions, PRs, issues, ideas.\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fgraphs\u002Fcontributors\">\n\u003Cimg src=\"https:\u002F\u002Fopencollective.com\u002Fsktime\u002Fcontributors.svg?width=600&button=false\" \u002F>\n\u003C\u002Fa>\n\u003Cbr>\n\n## :bulb: Project vision\n\n* **By the community, for the community** -- developed by a friendly and collaborative community.\n* The **right tool for the right task** -- helping users to diagnose their learning problem and suitable scientific model types.\n* **Embedded in state-of-art ecosystems** and **provider of interoperable interfaces** -- interoperable with [scikit-learn], [statsmodels], [tsfresh], and other community favorites.\n* **Rich model composition and reduction functionality** -- build tuning and feature extraction pipelines, solve forecasting tasks with [scikit-learn] regressors.\n* **Clean, descriptive specification syntax** -- based on modern object-oriented design principles for data science.\n* **Fair model assessment and benchmarking** -- build your models, inspect your models, check your models, and avoid pitfalls.\n* **Easily extensible** -- easy extension templates to add your own algorithms compatible with sktime's API.\n","## 欢迎来到 sktime\n\n\u003Ca href=\"https:\u002F\u002Fwww.sktime.net\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fdocs\u002Fsource\u002Fimages\u002Fsktime-logo.svg\" width=\"175\" align=\"right\" \u002F>\u003C\u002Fa>\n\n> 面向时间序列机器学习的统一接口\n\n:rocket: **版本 0.40.1 现已发布！** [请在此处查看发行说明](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html)。\n\nsktime 是一个用于 Python 中时间序列分析的库。它为多种时间序列学习任务提供了一个统一的接口。目前，这些任务包括预测、时间序列分类、聚类、异常\u002F变点检测等。它配备了[时间序列算法](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Festimator_overview.html)以及与 scikit-learn 兼容的工具，用于构建、调优和验证时间序列模型。\n\n[scikit-learn]: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002F\n\n|  | **[文档](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Fusers.html)** · **[教程](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Fexamples.html)** · **[发行说明](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Fchangelog.html)** |\n|---|---|\n| **开源** | [![BSD 3-clause](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-BSD%203--Clause-blue.svg)](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002FLICENSE) [![GC.OS 赞助](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FGC.OS-Sponsored%20Project-orange.svg?style=flat&colorA=0eac92&colorB=2077b4)](https:\u002F\u002Fgc-os-ai.github.io\u002F) |\n| **教程** | [![Binder](https:\u002F\u002Fmybinder.org\u002Fbadge_logo.svg)](https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fsktime\u002Fsktime\u002Fmain?filepath=examples) [![!youtube](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?logo=youtube&label=YouTube&message=tutorials&color=red)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |\n| **社区** | [![!discord](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?logo=discord&label=discord&message=chat&color=lightgreen)](https:\u002F\u002Fdiscord.com\u002Finvite\u002F54ACzaFsn7) [![!slack](https:\u002F\u002Fimg.shields.io\u002Fstatic\u002Fv1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fscikit-time\u002F)  |\n| **CI\u002FCD** | [![github-actions](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Factions\u002Fworkflow\u002Fstatus\u002Fsktime\u002Fsktime\u002Fwheels.yml?logo=github)](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Factions\u002Fworkflows\u002Fwheels.yml) [![readthedocs](https:\u002F\u002Fimg.shields.io\u002Freadthedocs\u002Fsktime?logo=readthedocs)](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002F?badge=latest) [![platform](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fpn\u002Fconda-forge\u002Fsktime)](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime) |\n| **代码** |  [![!pypi](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fsktime?color=orange)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fsktime\u002F) [![!conda](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fvn\u002Fconda-forge\u002Fsktime)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fsktime) [![!python-versions](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fsktime)](https:\u002F\u002Fwww.python.org\u002F) [![!black](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcode%20style-black-000000.svg)](https:\u002F\u002Fgithub.com\u002Fpsf\u002Fblack)  |\n| **下载量** | ![PyPI - 下载量](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdw\u002Fsktime) ![PyPI - 下载量](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fsktime) [![下载量](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsktime_sktime_readme_0f3ad8369eaa.png))](https:\u002F\u002Fpepy.tech\u002Fproject\u002Fsktime) |\n| **引用** | [![!zenodo](https:\u002F\u002Fzenodo.org\u002Fbadge\u002FDOI\u002F10.5281\u002Fzenodo.3749000.svg)](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.3749000) |\n\n## :books: 文档\n\n| 文档                        |                                                                |\n|--------------------------------------| -------------------------------------------------------------- |\n| :star: **[教程]**               | 刚接触 sktime？这里有一切你需要了解的内容！              |\n| :clipboard: **[Binder 笔记本]**   | 可以在浏览器中直接运行的示例笔记本。              |\n| :woman_technologist: **[示例]**  | 如何使用 sktime 及其功能。                             |\n| :scissors: **[扩展模板]** | 如何使用 sktime 的 API 构建你自己的估计器。            |\n| :control_knobs: **[API 参考]**  | sktime API 的详细参考。                        |\n| :tv: **[视频教程]**            | 我们的 2021 年 PyData Global 视频教程。      |\n| :hammer_and_wrench: **[变更日志]**  | 更改和版本历史。                                   |\n| :deciduous_tree: **[路线图]**       | sktime 的软件和社区发展规划。                                   |\n| :pencil: **[相关软件]**      | 相关软件列表。 |\n\n[tutorials]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Ftutorials.html\n[binder notebooks]: https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fsktime\u002Fsktime\u002Fmain?filepath=examples\n[examples]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fexamples.html\n[video tutorial]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime-tutorial-pydata-global-2021\n[api reference]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference.html\n[changelog]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html\n[roadmap]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Froadmap.html\n[related software]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Frelated_software.html\n\n## :speech_balloon: 提问的地方\n\n我们非常欢迎问题和反馈！我们坚信公开分享帮助的价值，因为这样可以让更广泛的受众从中受益。\n\n| 类型                            | 平台                               |\n| ------------------------------- | --------------------------------------- |\n| :bug: **错误报告**              | [GitHub 问题追踪器]                  |\n| :sparkles: **功能请求 & 想法** | [GitHub 问题追踪器]                       |\n| :woman_technologist: **使用问题**          | [GitHub 讨论] · [Stack Overflow] |\n| :speech_balloon: **一般讨论**        | [GitHub 讨论] |\n| :factory: **贡献 & 开发** | `dev-chat` 频道 · [Discord] |\n| :globe_with_meridians: **见面会和协作会议** | [Discord] - 周五 13 UTC，dev\u002Fmeet-ups 频道 |\n\n[github issue tracker]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fissues\n[github discussions]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fdiscussions\n[stack overflow]: https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fsktime\n[discord]: https:\u002F\u002Fdiscord.com\u002Finvite\u002F54ACzaFsn7\n\n## :dizzy: 功能\n我们的目标是全面提升时间序列分析生态系统的互操作性和易用性。sktime 为不同但相关的 时间序列学习任务提供了一个__统一的接口__。它包含[__专用的时间序列算法__](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Fstable\u002Festimator_overview.html)以及用于构建组合模型的工具，例如流水线、集成、调参和降维等，使用户能够将为某一任务设计的算法应用于其他任务。\n\nsktime 还提供了与相关库的**接口**，例如 [scikit-learn]、[statsmodels]、[tsfresh]、[PyOD] 和 [fbprophet] 等。\n\n[statsmodels]: https:\u002F\u002Fwww.statsmodels.org\u002Fstable\u002Findex.html  \n[tsfresh]: https:\u002F\u002Ftsfresh.readthedocs.io\u002Fen\u002Flatest\u002F  \n[pyod]: https:\u002F\u002Fpyod.readthedocs.io\u002Fen\u002Flatest\u002F  \n[fbprophet]: https:\u002F\u002Ffacebook.github.io\u002Fprophet\u002F\n\n| 模块 | 状态 | 链接 |\n|---|---|---|\n| **[预测]** | 稳定 | [教程](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fexamples\u002F01_forecasting.html) · [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fforecasting.html) · [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fforecasting.py)  |\n| **[时间序列分类]** | 稳定 | [教程](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fexamples\u002F02_classification.ipynb) · [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fclassification.html) · [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fclassification.py) |\n| **[时间序列回归]** | 稳定 | [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fregression.html) |\n| **[变换]** | 稳定 | [教程](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fexamples\u002F03_transformers.ipynb) · [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Ftransformations.html) · [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Ftransformer.py)  |\n| **[异常检测]** | 正在成熟 | [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fdetection.py) |\n| **[参数估计]** | 正在成熟 | [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fparam_est.html) · [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Ftransformer.py)  |\n| **[时间序列聚类]** | 正在成熟 | [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fclustering.html) · [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fclustering.py) |\n| **[时间序列距离\u002F核函数]** | 正在成熟 | [教程](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fexamples\u002F03_transformers.ipynb) · [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Fdists_kernels.html) · [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fdist_kern_panel.py) |\n| **[时间序列对齐]** | 实验性 | [API 参考](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fapi_reference\u002Falignment.html) · [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Falignment.py) |\n| **[时间序列分割器]** | 正在成熟 | [扩展模板](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002Fextension_templates\u002Fsplit.py) | |\n| **[分布与仿真]** | 实验性 |  |\n\n[forecasting]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fforecasting  \n[time series classification]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fclassification  \n[time series regression]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fregression  \n[time series clustering]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fclustering  \n[detection tasks]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fdetection  \n[time series distances\u002Fkernels]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fdists_kernels  \n[time series alignment]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Falignment  \n[transformations]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Ftransformations  \n[distributions and simulation]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fproba  \n[time series splitters]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fsplit  \n[parameter fitting]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fsktime\u002Fparam_est\n\n\n## :hourglass_flowing_sand: 安装 sktime\n如需故障排除及详细的安装说明，请参阅[文档](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Finstallation.html)。\n\n- **操作系统**: macOS X · Linux · Windows 8.1 或更高版本\n- **Python 版本**: Python 3.10、3.11、3.12 和 3.13（仅 64 位）\n- **包管理器**: [pip] · [conda]（通过 `conda-forge`）\n\n[pip]: https:\u002F\u002Fpip.pypa.io\u002Fen\u002Fstable\u002F  \n[conda]: https:\u002F\u002Fdocs.conda.io\u002Fen\u002Flatest\u002F\n\n### pip\n使用 pip 时，sktime 的发布形式包括源码包和二进制 wheel 文件。可用的 wheel 文件列表请见[此处](https:\u002F\u002Fpypi.org\u002Fsimple\u002Fsktime\u002F)。\n\n```bash\npip install sktime\n```\n\n或者，安装所有可选依赖项：\n\n```bash\npip install sktime[all_extras]\n```\n\n针对特定学习任务的精选软依赖集合：\n\n```bash\npip install sktime[forecasting]  # 仅安装预测相关的依赖\npip install sktime[forecasting,transformations]  # 同时安装预测器和变换器\n```\n\n类似的选项还包括：\n* `forecasting`\n* `transformations`\n* `classification`\n* `regression`\n* `clustering`\n* `param_est`\n* `networks`\n* `detection`\n* `alignment`\n\n注意：通常情况下，并不会安装某项学习任务的所有软依赖，而只是精选的一部分。\n\n### conda\n您也可以通过 `conda-forge` 通道从 `conda` 安装 sktime。包含构建配方和配置的 feedstock 维护在[这个 conda-forge 仓库](https:\u002F\u002Fgithub.com\u002Fconda-forge\u002Fsktime-feedstock)中。\n\n```bash\nconda install -c conda-forge sktime\n```\n\n或者，安装所有可选依赖项：\n\n```bash\nconda install -c conda-forge sktime-all-extras\n```\n\n（由于 `conda` 不支持依赖集，因此无法通过 `conda` 灵活选择软依赖项）\n\n## :zap: 快速入门\n\n### 预测\n\n``` python\nfrom sktime.datasets import load_airline\nfrom sktime.forecasting.base import ForecastingHorizon\nfrom sktime.forecasting.theta import ThetaForecaster\nfrom sktime.split import temporal_train_test_split\nfrom sktime.performance_metrics.forecasting import mean_absolute_percentage_error\n\ny = load_airline()\ny_train, y_test = temporal_train_test_split(y)\nfh = ForecastingHorizon(y_test.index, is_relative=False)\nforecaster = ThetaForecaster(sp=12)  # 每月季节性周期\nforecaster.fit(y_train)\ny_pred = forecaster.predict(fh)\nmean_absolute_percentage_error(y_test, y_pred)\n>>> 0.08661467738190656\n```\n\n### 时间序列分类\n\n```python\nfrom sktime.classification.interval_based import TimeSeriesForestClassifier\nfrom sktime.datasets import load_arrow_head\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nX, y = load_arrow_head()\nX_train, X_test, y_train, y_test = train_test_split(X, y)\nclassifier = TimeSeriesForestClassifier()\nclassifier.fit(X_train, y_train)\ny_pred = classifier.predict(X_test)\naccuracy_score(y_test, y_pred)\n>>> 0.8679245283018868\n```\n\n## :wave: 如何参与\n\n加入 sktime 社区的方式有很多。我们遵循 [all-contributors](https:\u002F\u002Fgithub.com\u002Fall-contributors\u002Fall-contributors) 规范：欢迎各种形式的贡献，而不仅仅是代码。\n\n| 文档              |                                                                |\n| -------------------------- | --------------------------------------------------------------        |\n| :gift_heart: **[贡献]**        | 如何为 sktime 做贡献。          |\n| :school_satchel:  **[导师计划]** | 刚接触开源项目？申请我们的导师计划吧！ |\n| :date: **[会议]** | 参与我们的讨论、教程、工作坊和冲刺活动！ |\n| :woman_mechanic:  **[开发者指南]**      | 如何进一步开发 sktime 的代码库。                             |\n| :construction: **[功能增强提案]** | 为 sktime 设计新功能。 |\n| :medal_sports: **[贡献者]** | 所有贡献者的列表。 |\n| :raising_hand: **[角色]** | 我们核心社区角色的概述。 |\n| :money_with_wings: **[捐赠]** | 资助 sktime 的维护与开发。 |\n| :classical_building: **[治理]** | sktime 社区如何以及由谁做出决策。   |\n\n[contribute]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fget_involved\u002Fcontributing.html\n[donate]: https:\u002F\u002Fopencollective.com\u002Fsktime\n[extension templates]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Ftree\u002Fmain\u002Fextension_templates\n[developer guides]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fdeveloper_guide.html\n[contributors]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fblob\u002Fmain\u002FCONTRIBUTORS.md\n[governance]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fget_involved\u002Fgovernance.html\n[mentoring]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fmentoring\n[meetings]: https:\u002F\u002Fcalendar.google.com\u002Fcalendar\u002Fu\u002F0\u002Fembed?src=sktime.toolbox@gmail.com&ctz=UTC\n[enhancement proposals]: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fenhancement-proposals\n[roles]: https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fabout\u002Fteam.html\n\n## :trophy: 名人堂\n\n感谢所有社区成员的精彩贡献、拉取请求、问题报告和创意想法。\n\n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fgraphs\u002Fcontributors\">\n\u003Cimg src=\"https:\u002F\u002Fopencollective.com\u002Fsktime\u002Fcontributors.svg?width=600&button=false\" \u002F>\n\u003C\u002Fa>\n\u003Cbr>\n\n## :bulb: 项目愿景\n\n* **由社区、为社区** -- 由友好且协作的社区共同开发。\n* **对症下药** -- 帮助用户诊断其学习问题，并选择合适的科学模型类型。\n* **嵌入最先进的生态系统** 并提供**互操作性接口** -- 与 [scikit-learn]、[statsmodels]、[tsfresh] 等社区常用工具无缝对接。\n* **丰富的模型组合与降维功能** -- 构建调优和特征提取流水线，利用 [scikit-learn] 回归器解决预测任务。\n* **简洁、描述性强的规范语法** -- 基于现代面向对象的数据科学设计原则。\n* **公正的模型评估与基准测试** -- 您可以构建、检查、验证自己的模型，同时避免常见陷阱。\n* **易于扩展** -- 提供便捷的扩展模板，方便您添加与 sktime API 兼容的自定义算法。","# sktime 快速上手指南\n\n## 环境准备\n\n### 系统要求\n- 操作系统：macOS X、Linux、Windows 8.1 或更高版本\n- Python 版本：Python 3.10、3.11、3.12 和 3.13（仅支持 64 位）\n\n### 前置依赖\nsktime 依赖于以下常用 Python 库：\n- `numpy`\n- `scipy`\n- `pandas`\n- `scikit-learn`\n\n建议使用国内镜像源加速安装，例如：\n```bash\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n## 安装步骤\n\n### 使用 pip 安装\n```bash\npip install sktime\n```\n\n或安装所有可选依赖：\n```bash\npip install sktime[all_extras]\n```\n\n根据需要安装特定功能的依赖：\n```bash\npip install sktime[forecasting]          # 预测相关依赖\npip install sktime[forecasting,transformations]  # 预测和转换相关依赖\n```\n\n### 使用 conda 安装（推荐）\n```bash\nconda install -c conda-forge sktime\n```\n\n## 基本使用\n\n以下是一个最简单的示例，演示如何使用 sktime 进行时间序列预测：\n\n```python\nfrom sktime.forecasting.arima import AutoARIMA\nfrom sktime.datasets import load_airline\n\n# 加载数据集\ny = load_airline()\n\n# 初始化模型\nforecaster = AutoARIMA()\n\n# 拟合模型\nforecaster.fit(y)\n\n# 预测未来 12 个时间点\ny_pred = forecaster.predict(fh=12)\n\nprint(y_pred)\n```\n\n此示例展示了如何使用 sktime 的 `AutoARIMA` 模型进行时间序列预测。更多用法请参考 [官方文档](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fexamples.html)。","某制造业公司的数据团队需要对设备的传感器数据进行时间序列分类，以识别设备的不同运行状态。他们需要快速构建和验证模型，但面临多个工具和库的整合难题。\n\n### 没有 sktime 时  \n- 需要分别使用不同的库处理时间序列分类、特征提取和模型评估，导致代码冗余  \n- 缺乏统一的接口，不同算法之间的切换和比较困难  \n- 数据预处理和模型训练流程不一致，难以维护和扩展  \n- 没有内置的时间序列专用评估指标，需手动实现  \n\n### 使用 sktime 后  \n- 提供统一的 API，简化了分类、特征工程和模型评估的流程  \n- 支持多种时间序列分类算法，便于快速实验和比较  \n- 内置时间序列专用的评估方法，提升模型验证的准确性  \n- 与 scikit-learn 兼容，方便集成到现有机器学习工作流中  \n\nsktime 通过统一的接口和丰富的功能，显著提升了时间序列任务的开发效率和模型质量。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fsktime_sktime_0aec33ad.png","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fsktime_7e57e590.jpg",null,"info@sktime.net","https:\u002F\u002Fwww.sktime.net","https:\u002F\u002Fgithub.com\u002Fsktime",[81,85,89,93,97,100,103],{"name":82,"color":83,"percentage":84},"Python","#3572A5",99.7,{"name":86,"color":87,"percentage":88},"Jupyter Notebook","#DA5B0B",0.2,{"name":90,"color":91,"percentage":92},"CSS","#663399",0.1,{"name":94,"color":95,"percentage":96},"MATLAB","#e16737",0,{"name":98,"color":99,"percentage":96},"Makefile","#427819",{"name":101,"color":102,"percentage":96},"Dockerfile","#384d54",{"name":104,"color":105,"percentage":96},"Shell","#89e051",9690,2058,"2026-04-05T09:24:34","BSD-3-Clause",1,"Linux, macOS, Windows","未说明",{"notes":114,"python":115,"dependencies":116},"建议使用 conda 管理环境，首次运行需下载约 5GB 模型文件","3.10, 3.11, 3.12, 3.13",[117,118,119,120,121,122,123,124,125],"scikit-learn","numpy","pandas","joblib","pyyaml","setuptools","wheel","packaging","importlib-metadata",[15,14,51,54,13],[128,129,117,130,131,132,133,134,135,136,137,138,139,67,140],"time-series","machine-learning","time-series-classification","time-series-regression","forecasting","time-series-analysis","data-science","data-mining","hacktoberfest","ai","anomaly-detection","changepoint-detection","time-series-segmentation",4,"2026-03-27T02:49:30.150509","2026-04-06T05:17:20.814671",[145,150,155,160,164,168],{"id":146,"question_zh":147,"answer_zh":148,"source_url":149},5406,"如何将 numba 转换为 sktime 的软依赖？","`numba` 已经从 0.18.0 版本开始作为软依赖。这意味着用户可以选择是否安装 `numba`，不影响 `sktime` 的正常使用。对于已安装 `numba` 的用户，体验保持不变；未安装的用户也可以通过 `all_estimators` 查找基于 `numba` 的估计器。","https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fissues\u002F3567",{"id":151,"question_zh":152,"answer_zh":153,"source_url":154},5407,"如何避免 GitHub Actions 在所有 fork 上运行 cron job？","目前 GitHub Actions 默认会在所有 fork 上运行 cron job，这会导致资源浪费。建议修改 GitHub Actions 配置，仅在主仓库（`sktime\u002Fsktime`）上运行这些任务。可以参考 GitHub 社区讨论中的解决方案：https:\u002F\u002Fgithub.com\u002Forgs\u002Fcommunity\u002Fdiscussions\u002F26704。","https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fissues\u002F9027",{"id":156,"question_zh":157,"answer_zh":158,"source_url":159},5408,"如何在 sktime 中自动更新算法概述表？","可以通过 GitHub Actions 自动更新算法概述表。具体实现方式是使用 Sphinx 和 Read the Docs，在构建过程中运行自定义函数生成表格，并添加 JavaScript 实现搜索功能。相关实现可参考 #1138 PR。","https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fissues\u002F704",{"id":161,"question_zh":162,"answer_zh":163,"source_url":149},5409,"如何将 `numba` 进一步隔离到分类模块中？","目前 `numba` 已作为软依赖，但未来可能进一步隔离到分类模块中，即只有在使用 `numba` 相关分类器时才需要导入 `numba`。相关实验性 PR 可以参考：https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F3843。",{"id":165,"question_zh":166,"answer_zh":167,"source_url":149},5410,"如何解决 `numba` 编译问题导致的 Forecasting 代码无法运行？","在 0.14.1 版本中，通过隔离 `numba` 调用解决了该问题。确保使用 0.14.1 或更高版本，并检查是否有写权限问题。如果仍有问题，请提交详细信息以便排查。",{"id":169,"question_zh":170,"answer_zh":171,"source_url":159},5411,"如何在 sktime 中查找特定类型的估计器？","可以使用 `sktime.utils.all_estimators()` 函数，传入 `estimator_types` 参数指定类型（如 `classifier`、`forecaster` 等）。例如：`all_estimators(estimator_types=\"classifier\")`。",[173,178,183,188,193,198,203,208,213,218,223,228,233,238,243,248,253,258,263,268],{"id":174,"version":175,"summary_zh":176,"released_at":177},104879,"v0.40.1","Mini-release to correct Zenodo record.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.40.0...v0.40.1","2025-11-25T00:02:03",{"id":179,"version":180,"summary_zh":181,"released_at":182},104880,"v0.40.0","Feature release and python 3.14 compatibility.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n@Abelarm,\r\n@AdKnow,\r\n@Akai01,\r\n@alphaleporus,\r\n@AryanDhanuka10,\r\n@Astrael1,\r\n@Faakhir30,\r\n@fkiraly,\r\n@JATAYU000,\r\n@jgyasu,\r\n@leonicus,\r\n@mabuimo,\r\n@martinloretzzz,\r\n@Navya-1817,\r\n@noxthot,\r\n@omkar-334,\r\n@poopsiclepooding,\r\n@pranavvp16,\r\n@Priyanshu1303d,\r\n@RecreationalMath,\r\n@Sanchay117,\r\n@sanskarmodi8,\r\n@SimonBlanke,\r\n@swetha3456,\r\n@Tanuj-Taneja1,\r\n@XAheli,\r\n@yarnabrina\r\n\r\n## New Contributors\r\n* @swetha3456 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7995\r\n* @noxthot made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8891\r\n* @Priyanshu1303d made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8911\r\n* @AryanDhanuka10 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8865\r\n* @leonicus made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8924\r\n* @poopsiclepooding made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8838\r\n* @Navya-1817 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8919\r\n* @omkar-334 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8940\r\n* @SimonBlanke made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8965\r\n* @mabuimo made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8981\r\n* @RecreationalMath made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8842\r\n* @AdKnow made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8982\r\n* @martinloretzzz made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8876\r\n* @Faakhir30 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F9067\r\n* @alphaleporus made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F9086\r\n* @Akai01 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F9062\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.39.0...v0.40.0","2025-11-23T19:06:53",{"id":184,"version":185,"summary_zh":186,"released_at":187},104881,"v0.39.0","Release focusing on:\r\n\r\n* python 3.9 end-of-life\r\n* changes to tag framework, testing framework\r\n* scheduled deprecations\r\n\r\nAlso includes new estimators and feature improvements.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@Astrael1,\r\n@ericjb,\r\n@felipeangelimvieira,\r\n@fkiraly,\r\n@JATAYU000,\r\n@jgyasu,\r\n@oresthes,\r\n@piyushbiraje,\r\n@RobKuebler,\r\n@SimonBlanke,\r\n@sinemkilicdere,\r\n@Tanuj-Taneja1,\r\n@tingiskhan,\r\n@yarnabrina\r\n\r\n## New Contributors\r\n* @JATAYU000 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8764\r\n* @oresthes made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8711\r\n* @Astrael1 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8853\r\n* @Tanuj-Taneja1 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8761\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.38.5...v0.39.0","2025-09-25T22:16:12",{"id":189,"version":190,"summary_zh":191,"released_at":192},104882,"v0.38.5","Feature release.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@aarsh1a,\r\n@aarushitandon0,\r\n@abhishek-iitmadras,\r\n@adityagarwal15,\r\n@akshathmangudi,\r\n@Alexis-Fauxbaton,\r\n@Anamika457,\r\n@Burnie-Murray,\r\n@fkiraly,\r\n@garar,\r\n@HarshvirSandhu,\r\n@hudeqiWH,\r\n@Imvedansh,\r\n@IsaiasRT,\r\n@jgyasu,\r\n@jnrusson1,\r\n@julian-fong,\r\n@LHoelper,\r\n@Mujeeb4,\r\n@OnePunchMonk,\r\n@Pradyumn-cloud,\r\n@Pranavsingh431,\r\n@Rajdeep-naha,\r\n@Sanchay117,\r\n@sinemkilicdere,\r\n@Sohaib-Ahmed21,\r\n@szepeviktor,\r\n@SzymonStolarski,\r\n@thearshkumar,\r\n@thisisrick25,\r\n@Virgile-Foussereau,\r\n@XAheli,\r\n@yarnabrina\r\n\r\n\r\n## New Contributors\r\n* @aarsh1a made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8386\r\n* @Pradyumn-cloud made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8636\r\n* @OnePunchMonk made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8335\r\n* @SzymonStolarski made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8610\r\n* @Anamika457 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8599\r\n* @aarushitandon0 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8286\r\n* @garar made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8660\r\n* @XAheli made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8502\r\n* @sinemkilicdere made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8603\r\n* @akshathmangudi made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8628\r\n* @Sanchay117 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8622\r\n* @Virgile-Foussereau made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8509\r\n* @hudeqiWH made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8689\r\n* @Mujeeb4 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8132\r\n* @Alexis-Fauxbaton made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8626\r\n* @adityagarwal15 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8304\r\n* @jnrusson1 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8721\r\n* @Imvedansh made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8329\r\n* @Rajdeep-naha made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8328\r\n* @thisisrick25 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8737\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.38.4...v0.38.5","2025-08-24T20:13:00",{"id":194,"version":195,"summary_zh":196,"released_at":197},104883,"v0.38.4","## What's Changed\r\n\r\nRelease focusing on bugfixes, `scikit-learn` downwards compatibility after 1.7.0,\r\nand increasing test framework reliability.\r\n\r\nFor last larger feature update, see [0.37.1](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.37.1).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@Burnie-Murray,\r\n@ericjb,\r\n@fkiraly,\r\n@Ghxst07,\r\n@IsaiasRT,\r\n@jgyasu,\r\n@julianfong,\r\n@phoeenniixx,\r\n@thearshkumar,\r\n@Tirath5504,\r\n@xenonnn4w\r\n\r\n## New Contributors\r\n* @thearshkumar made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8511\r\n* @xenonnn4w made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8516\r\n* @Ghxst07 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8527\r\n* @IsaiasRT made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8520\r\n* @Tirath5504 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8539\r\n* @Burnie-Murray made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8564\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.38.3...v0.38.4","2025-07-17T16:23:37",{"id":199,"version":200,"summary_zh":201,"released_at":202},104884,"v0.38.3","## What's Changed\r\n\r\nHotfix for `ChronosForecaster` and `TinyTimeMixerForecaster` after `transformers 4.53` compatibility patch.\r\n\r\nFor last larger feature update, see [0.37.1](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.37.1).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.38.2...v0.38.3","2025-07-04T19:58:02",{"id":204,"version":205,"summary_zh":206,"released_at":207},104885,"v0.38.2","## What's Changed\r\n\r\n`transformers 4.53` compatibility patch, minor maintenance and documentation updates.\r\n\r\nFor last larger feature update, see [0.37.1](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.37.1).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@abhishek-iitmadras,\r\n@fkiraly,\r\n@yarnabrina\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.38.1...v0.38.2","2025-07-03T18:37:39",{"id":209,"version":210,"summary_zh":211,"released_at":212},104886,"v0.38.1","## What's Changed\r\n\r\n`scikit-learn 1.7` compatibility hotfix, thanks go to @andretheronsa.\r\n\r\nFor last larger feature update, see [0.37.1](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.37.1).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@andretheronsa,\r\n@fkiraly,\r\n@yarnabrina\r\n\r\n## New Contributors\r\n* @andretheronsa made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8459\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.38.0...v0.38.1","2025-06-26T14:38:02",{"id":214,"version":215,"summary_zh":216,"released_at":217},104887,"v0.38.0","## What's Changed\r\n\r\nMaintenance release with scheduled deprecations and change actions.\r\n\r\nFor last larger feature update, see [0.37.1](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.37.1).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@felipeangelimvieira,\r\n@fkiraly,\r\n@jgyasu,\r\n@yarnabrina\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.37.1...v0.38.0","2025-06-24T20:01:31",{"id":219,"version":220,"summary_zh":221,"released_at":222},104888,"v0.37.1","Feature release.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@amanmogal,\r\n@andoriyaprashant,\r\n@Ankit-1204,\r\n@benHeid,\r\n@felipeangelimvieira,\r\n@fkiraly,\r\n@gbilleyPeco,\r\n@HarshvirSandhu,\r\n@jgyasu,\r\n@jobs-git,\r\n@julian-fong,\r\n@ksharma6,\r\n@marrov,\r\n@mateuszkasprowicz,\r\n@mohamed-halemo,\r\n@nahcol10,\r\n@nmwitzig,\r\n@OmBiradar,\r\n@pcpp94,\r\n@piyushbiraje,\r\n@Prahitha,\r\n@Pranavsingh431,\r\n@RobKuebler,\r\n@szepeviktor,\r\n@Tveten,\r\n@vedantag17,\r\n@yury-fedotov\r\n\r\n## New Contributors\r\n\r\n* @pcpp94 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8214\r\n* @OmBiradar made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8124\r\n* @Tveten made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7509\r\n* @yury-fedotov made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8275\r\n* @jobs-git made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8269\r\n* @nahcol10 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8255\r\n* @Pranavsingh431 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8285\r\n* @Prahitha made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7993\r\n* @piyushbiraje made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8339\r\n* @andoriyaprashant made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8293\r\n* @nmwitzig made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8337\r\n* @mohamed-halemo made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8389\r\n* @amanmogal made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8395\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.37.0...v0.37.1","2025-06-22T23:53:25",{"id":224,"version":225,"summary_zh":226,"released_at":227},104889,"v0.37.0","## What's Changed\r\n\r\nMaintenance release with scheduled deprecations and change actions.\r\n\r\nFor last larger feature update, see [0.36.1](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.36.1).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@fkiraly,\r\n@jgyasu\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.36.1...v0.37.0","2025-04-12T14:36:31",{"id":229,"version":230,"summary_zh":231,"released_at":232},104890,"v0.36.1","## What's Changed\r\n\r\nFeature release.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@amitsubhashchejara,\r\n@Ankit-1204,\r\n@benHeid,\r\n@Bergschaf,\r\n@celestinoxp,\r\n@danferns,\r\n@ericjb,\r\n@felipeangelimvieira,\r\n@fkiraly,\r\n@geetu040,\r\n@haroon0x,\r\n@hazrulakmal,\r\n@itsbharatj,\r\n@jgyasu,\r\n@LHoelper,\r\n@LorchZachery,\r\n@oseiskar,\r\n@phoeenniixx,\r\n@PranavBhatP,\r\n@Reckadon,\r\n@satvshr,\r\n@Sohaib-Ahmed21,\r\n@Spinachboul,\r\n@tapyu,\r\n@TomatoChocolate12,\r\n@Utkarsh-Aggarwal,\r\n@vedantag17,\r\n@wilsbj,\r\n@XinyuWuu,\r\n@yarnabrina\r\n\r\n## New Contributors\r\n* @Bergschaf made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7814\r\n* @Utkarsh-Aggarwal made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7602\r\n* @Reckadon made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7865\r\n* @celestinoxp made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7903\r\n* @itsbharatj made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7884\r\n* @TomatoChocolate12 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7684\r\n* @haroon0x made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7795\r\n* @wilsbj made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7992\r\n* @LHoelper made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7954\r\n* @oseiskar made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8029\r\n* @tapyu made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8044\r\n* @danferns made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8094\r\n* @amitsubhashchejara made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F8099\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.36.0...v0.36.1","2025-04-04T23:12:45",{"id":234,"version":235,"summary_zh":236,"released_at":237},104891,"v0.36.0","## What's Changed\r\n\r\nMaintenance release with `scikit-learn 1.6.X` and `numpy 2.2.X` compatibility.\r\n\r\nFor the last non-maintenance content update, see 0.35.1.\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.35.1...v0.36.0","2025-02-05T08:19:17",{"id":239,"version":240,"summary_zh":241,"released_at":242},104892,"v0.35.1","## What's Changed\r\n\r\nFeature release.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@Abelarm,\r\n@abhishek-iitmadras,\r\n@Adarsh2345,\r\n@Alex-JG3,\r\n@Ankit-1204,\r\n@b9junkers,\r\n@benHeid,\r\n@cheachu,\r\n@Dehelaan,\r\n@felipeangelimvieira,\r\n@fkiraly,\r\n@fnhirwa,\r\n@gavinkatz001,\r\n@gbilleyPeco,\r\n@geetu040,\r\n@HarshvirSandhu,\r\n@jgyasu,\r\n@keitaVigano,\r\n@KrishBakshi,\r\n@ksharma6,\r\n@lenaklosik,\r\n@marcosfelt,\r\n@marrov,\r\n@mateuszkasprowicz,\r\n@phoeenniixx,\r\n@PranavBhatP,\r\n@RHYTHM2405,\r\n@RUPESH-KUMAR01,\r\n@SABARNO-PRAMANICK,\r\n@Salzemann,\r\n@sanskarmodi8,\r\n@satvshr,\r\n@seigpe,\r\n@skinan,\r\n@Spinachboul,\r\n@tanvincible,\r\n@VjayRam,\r\n@y-mx,\r\n@yarnabrina\r\n\r\n## New Contributors\r\n* @skinan made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7508\r\n* @Adarsh2345 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7381\r\n* @y-mx made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7480\r\n* @SABARNO-PRAMANICK made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7494\r\n* @jgyasu made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7487\r\n* @VjayRam made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7540\r\n* @b9junkers made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7539\r\n* @RUPESH-KUMAR01 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7553\r\n* @PranavBhatP made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7527\r\n* @marcosfelt made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7541\r\n* @tanvincible made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7469\r\n* @lenaklosik made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7535\r\n* @satvshr made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7615\r\n* @cheachu made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7654\r\n* @RHYTHM2405 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7647\r\n* @gbilleyPeco made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7639\r\n* @KrishBakshi made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7673\r\n* @Salzemann made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7706\r\n* @seigpe made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7592\r\n* @abhishek-iitmadras made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7692\r\n* @HarshvirSandhu made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7606\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.35.0...v0.35.1","2025-02-02T23:57:55",{"id":244,"version":245,"summary_zh":246,"released_at":247},104893,"v0.35.0","## What's Changed\r\n\r\nMaintenance release with scheduled deprecations and change actions.\r\n\r\nFor last larger feature update, see [0.34.1](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.34.1).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@fkiraly,\r\n@fnhirwa,\r\n@yarnabrina\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.34.1...v0.35.0","2024-12-09T16:13:44",{"id":249,"version":250,"summary_zh":251,"released_at":252},104894,"v0.34.1","## What's Changed\r\n\r\nFeature release.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@Akhsuna07,\r\n@Alex-JG3,\r\n@alyssadsouza,\r\n@Dehelaan,\r\n@ericjb,\r\n@fkiraly,\r\n@gavinkatz001,\r\n@geetu040,\r\n@hudsonhoch,\r\n@jgyfutub,\r\n@julian-fong,\r\n@jusssch,\r\n@keitaVigano,\r\n@liya-zhu,\r\n@manolotis,\r\n@MarkusSagen,\r\n@medha-14,\r\n@mjste,\r\n@pranavvp16,\r\n@sanskarmodi8,\r\n@ShivamJ07,\r\n@Sohaib-Ahmed21,\r\n@SSROCKS30,\r\n@tajir0,\r\n@talat-khattatov,\r\n@vagechirkov,\r\n@VectorNd,\r\n@yarnabrina\r\n\r\n## New Contributors\r\n* @jusssch made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7335\r\n* @SSROCKS30 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7327\r\n* @Akhsuna07 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7322\r\n* @medha-14 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7344\r\n* @manolotis made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7366\r\n* @VectorNd made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7320\r\n* @jgyfutub made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7361\r\n* @mjste made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7401\r\n* @gavinkatz001 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7389\r\n* @keitaVigano made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7376\r\n* @ShivamJ07 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7425\r\n* @alyssadsouza made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7432\r\n* @tajir0 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7420\r\n* @liya-zhu made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7443\r\n* @vagechirkov made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7353\r\n* @Sohaib-Ahmed21 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7400\r\n* @hudsonhoch made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7417\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.34.0...v0.34.1","2024-12-03T12:11:35",{"id":254,"version":255,"summary_zh":256,"released_at":257},104895,"v0.34.0","## What's Changed\r\n\r\nMaintenance release with:\r\n\r\n* full `python 3.13` support\r\n* scheduled deprecations and change actions.\r\n\r\nFor last larger feature updates, see [0.33.2](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.33.2).\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.33.2...v0.34.0","2024-10-19T19:34:17",{"id":259,"version":260,"summary_zh":261,"released_at":262},104896,"v0.33.2","## What's Changed\r\n\r\nFeature release.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@alex-jg3,\r\n@Anuragwagh,\r\n@benHeid,\r\n@Dehelaan,\r\n@ericjb,\r\n@fkiraly,\r\n@fnhirwa,\r\n@Garve,\r\n@geetu040,\r\n@Humorloos,\r\n@jan-mue,\r\n@julian-fong,\r\n@KarlKolibri,\r\n@MarkusSagen,\r\n@ninedigits,\r\n@phoeenniixx,\r\n@Prtm2110,\r\n@RobotPsychologist,\r\n@rigvedmanoj,\r\n@SaiRevanth25,\r\n@sanskarmodi8,\r\n@shivanshsinghal-22,\r\n@Smoothengineer,\r\n@talat-khattatov,\r\n@tianjiqx,\r\n@vedantag17,\r\n@XinyuWuu,\r\n@Z-Fran\r\n\r\n## New Contributors\r\n\r\n* @rigvedmanoj made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7001\r\n* @vedantag17 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7171\r\n* @jan-mue made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7181\r\n* @Humorloos made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7180\r\n* @KarlKolibri made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7178\r\n* @Dehelaan made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7185\r\n* @shivanshsinghal-22 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7221\r\n* @Anuragwagh made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7199\r\n* @Smoothengineer made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7262\r\n* @sanskarmodi8 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7260\r\n* @MarkusSagen made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7193\r\n* @Prtm2110 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7212\r\n* @RobotPsychologist made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7283\r\n* @talat-khattatov made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7278\r\n* @Garve made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7267\r\n* @madhuri723 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F7042\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.33.1...v0.33.2","2024-10-18T10:51:55",{"id":264,"version":265,"summary_zh":266,"released_at":267},104897,"v0.33.1","## What's Changed\r\n\r\nFeature release.\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@ankit-1204,\r\n@benHeid,\r\n@ericjb,\r\n@fkiraly,\r\n@pranavvp16,\r\n@SaiRevanth25,\r\n@Saptarshi-Bandopadhyay,\r\n@XinyuWuu,\r\n@yarnabrina\r\n\r\n## New Contributors\r\n* @Ankit-1204 made their first contribution in https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fpull\u002F6782\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.33.0...v0.33.1","2024-09-26T08:09:31",{"id":269,"version":270,"summary_zh":271,"released_at":272},104898,"v0.33.0","## What's Changed\r\n\r\nMaintenance release with scheduled deprecations and change actions.\r\nFor last larger feature updates, see [0.32.4](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.32.4) and [0.32.2](https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Freleases\u002Ftag\u002Fv0.32.2)\r\n\r\nPlease see our [changelog](https:\u002F\u002Fwww.sktime.net\u002Fen\u002Flatest\u002Fchangelog.html) for a description of all changes.\r\n\r\n## All Contributors\r\n\r\n@benHeid,\r\n@ericjb,\r\n@fkiraly,\r\n@SaiRevanth25,\r\n@Saptarshi-Bandopadhyay\r\n\r\n**Full Changelog**: https:\u002F\u002Fgithub.com\u002Fsktime\u002Fsktime\u002Fcompare\u002Fv0.32.4...v0.33.0","2024-09-10T06:36:10"]