{"id":13468844,"url":"https://github.com/sktime/sktime","last_synced_at":"2025-09-09T19:54:17.292Z","repository":{"id":37369686,"uuid":"156401841","full_name":"sktime/sktime","owner":"sktime","description":"A unified framework for machine learning with time series","archived":false,"fork":false,"pushed_at":"2025-05-01T18:42:38.000Z","size":80630,"stargazers_count":8368,"open_issues_count":1449,"forks_count":1531,"subscribers_count":106,"default_branch":"main","last_synced_at":"2025-05-05T05:52:53.259Z","etag":null,"topics":["ai","anomaly-detection","changepoint-detection","data-mining","data-science","forecasting","hacktoberfest","machine-learning","scikit-learn","sktime","time-series","time-series-analysis","time-series-classification","time-series-regression","time-series-segmentation"],"latest_commit_sha":null,"homepage":"https://www.sktime.net","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"bsd-3-clause","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sktime.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":null,"support":null,"governance":"GOVERNANCE.md","roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"github":"sktime","patreon":null,"open_collective":"sktime","ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"custom":null}},"created_at":"2018-11-06T15:08:24.000Z","updated_at":"2025-05-04T12:37:51.000Z","dependencies_parsed_at":"2023-12-19T09:41:39.756Z","dependency_job_id":"5ed6bc10-dcf6-4029-98a8-89d776eb3d85","html_url":"https://github.com/sktime/sktime","commit_stats":{"total_commits":4638,"total_committers":430,"mean_commits":"10.786046511627907","dds":0.685640362225097,"last_synced_commit":"3e8ecf84e1832030cdb69640ab30382f699850e2"},"previous_names":["alan-turing-institute/sktime"],"tags_count":90,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sktime%2Fsktime/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sktime","download_url":"https://codeload.github.com/sktime/sktime/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252457811,"owners_count":21750899,"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":["ai","anomaly-detection","changepoint-detection","data-mining","data-science","forecasting","hacktoberfest","machine-learning","scikit-learn","sktime","time-series","time-series-analysis","time-series-classification","time-series-regression","time-series-segmentation"],"created_at":"2024-07-31T15:01:20.177Z","updated_at":"2025-09-09T19:54:17.277Z","avatar_url":"https://github.com/sktime.png","language":"Python","readme":"\n## Welcome to sktime\n\n\u003ca href=\"https://www.sktime.net\"\u003e\u003cimg src=\"https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.svg\" width=\"175\" align=\"right\" /\u003e\u003c/a\u003e\n\n\u003e A unified interface for machine learning with time series\n\n:rocket: **Version 0.38.5 out now!** [Check out the release notes here](https://www.sktime.net/en/latest/changelog.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/changepoint detection, and other tasks. It comes with [time series algorithms](https://www.sktime.net/en/stable/estimator_overview.html) and [scikit-learn] compatible tools to build, tune, and validate time series models.\n\n[scikit-learn]: https://scikit-learn.org/stable/\n\n|  | **[Documentation](https://www.sktime.net/en/stable/users.html)** · **[Tutorials](https://www.sktime.net/en/stable/examples.html)** · **[Release Notes](https://www.sktime.net/en/stable/changelog.html)** |\n|---|---|\n| **Open\u0026#160;Source** | [![BSD 3-clause](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/sktime/sktime/blob/main/LICENSE) [![GC.OS Sponsored](https://img.shields.io/badge/GC.OS-Sponsored%20Project-orange.svg?style=flat\u0026colorA=0eac92\u0026colorB=2077b4)](https://gc-os-ai.github.io/) |\n| **Tutorials** | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples) [![!youtube](https://img.shields.io/static/v1?logo=youtube\u0026label=YouTube\u0026message=tutorials\u0026color=red)](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |\n| **Community** | [![!discord](https://img.shields.io/static/v1?logo=discord\u0026label=discord\u0026message=chat\u0026color=lightgreen)](https://discord.com/invite/54ACzaFsn7) [![!slack](https://img.shields.io/static/v1?logo=linkedin\u0026label=LinkedIn\u0026message=news\u0026color=lightblue)](https://www.linkedin.com/company/scikit-time/)  |\n| **CI/CD** | [![github-actions](https://img.shields.io/github/actions/workflow/status/sktime/sktime/wheels.yml?logo=github)](https://github.com/sktime/sktime/actions/workflows/wheels.yml) [![readthedocs](https://img.shields.io/readthedocs/sktime?logo=readthedocs)](https://www.sktime.net/en/latest/?badge=latest) [![platform](https://img.shields.io/conda/pn/conda-forge/sktime)](https://github.com/sktime/sktime) |\n| **Code** |  [![!pypi](https://img.shields.io/pypi/v/sktime?color=orange)](https://pypi.org/project/sktime/) [![!conda](https://img.shields.io/conda/vn/conda-forge/sktime)](https://anaconda.org/conda-forge/sktime) [![!python-versions](https://img.shields.io/pypi/pyversions/sktime)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)  |\n| **Downloads** | ![PyPI - Downloads](https://img.shields.io/pypi/dw/sktime) ![PyPI - Downloads](https://img.shields.io/pypi/dm/sktime) [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=total\u0026units=international_system\u0026left_color=grey\u0026right_color=blue\u0026left_text=cumulative%20(pypi))](https://pepy.tech/project/sktime) |\n| **Citation** | [![!zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.3749000.svg)](https://doi.org/10.5281/zenodo.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://www.sktime.net/en/latest/tutorials.html\n[binder notebooks]: https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples\n[examples]: https://www.sktime.net/en/latest/examples.html\n[video tutorial]: https://github.com/sktime/sktime-tutorial-pydata-global-2021\n[api reference]: https://www.sktime.net/en/latest/api_reference.html\n[changelog]: https://www.sktime.net/en/latest/changelog.html\n[roadmap]: https://www.sktime.net/en/latest/roadmap.html\n[related software]: https://www.sktime.net/en/latest/related_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 \u0026 Ideas** | [GitHub Issue Tracker]                       |\n| :woman_technologist: **Usage Questions**          | [GitHub Discussions] · [Stack Overflow] |\n| :speech_balloon: **General Discussion**        | [GitHub Discussions] |\n| :factory: **Contribution \u0026 Development** | `dev-chat` channel · [Discord] |\n| :globe_with_meridians: **Meet-ups and collaboration sessions** | [Discord] - Fridays 13 UTC, dev/meet-ups channel |\n\n[github issue tracker]: https://github.com/sktime/sktime/issues\n[github discussions]: https://github.com/sktime/sktime/discussions\n[stack overflow]: https://stackoverflow.com/questions/tagged/sktime\n[discord]: https://discord.com/invite/54ACzaFsn7\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://www.sktime.net/en/stable/estimator_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://www.statsmodels.org/stable/index.html\n[tsfresh]: https://tsfresh.readthedocs.io/en/latest/\n[pyod]: https://pyod.readthedocs.io/en/latest/\n[fbprophet]: https://facebook.github.io/prophet/\n\n| Module | Status | Links |\n|---|---|---|\n| **[Forecasting]** | stable | [Tutorial](https://www.sktime.net/en/latest/examples/01_forecasting.html) · [API Reference](https://www.sktime.net/en/latest/api_reference/forecasting.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/forecasting.py)  |\n| **[Time Series Classification]** | stable | [Tutorial](https://github.com/sktime/sktime/blob/main/examples/02_classification.ipynb) · [API Reference](https://www.sktime.net/en/latest/api_reference/classification.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/classification.py) |\n| **[Time Series Regression]** | stable | [API Reference](https://www.sktime.net/en/latest/api_reference/regression.html) |\n| **[Transformations]** | stable | [Tutorial](https://github.com/sktime/sktime/blob/main/examples/03_transformers.ipynb) · [API Reference](https://www.sktime.net/en/latest/api_reference/transformations.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/transformer.py)  |\n| **[Detection tasks]** | maturing | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/detection.py) |\n| **[Parameter fitting]** | maturing | [API Reference](https://www.sktime.net/en/latest/api_reference/param_est.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/transformer.py)  |\n| **[Time Series Clustering]** | maturing | [API Reference](https://www.sktime.net/en/latest/api_reference/clustering.html) ·  [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/clustering.py) |\n| **[Time Series Distances/Kernels]** | maturing | [Tutorial](https://github.com/sktime/sktime/blob/main/examples/03_transformers.ipynb) · [API Reference](https://www.sktime.net/en/latest/api_reference/dists_kernels.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/dist_kern_panel.py) |\n| **[Time Series Alignment]** | experimental | [API Reference](https://www.sktime.net/en/latest/api_reference/alignment.html) · [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/alignment.py) |\n| **[Time Series Splitters]** | maturing | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/split.py) | |\n| **[Distributions and simulation]** | experimental |  |\n\n[forecasting]: https://github.com/sktime/sktime/tree/main/sktime/forecasting\n[time series classification]: https://github.com/sktime/sktime/tree/main/sktime/classification\n[time series regression]: https://github.com/sktime/sktime/tree/main/sktime/regression\n[time series clustering]: https://github.com/sktime/sktime/tree/main/sktime/clustering\n[detection tasks]: https://github.com/sktime/sktime/tree/main/sktime/detection\n[time series distances/kernels]: https://github.com/sktime/sktime/tree/main/sktime/dists_kernels\n[time series alignment]: https://github.com/sktime/sktime/tree/main/sktime/alignment\n[transformations]: https://github.com/sktime/sktime/tree/main/sktime/transformations\n[distributions and simulation]: https://github.com/sktime/sktime/tree/main/sktime/proba\n[time series splitters]: https://github.com/sktime/sktime/tree/main/sktime/split\n[parameter fitting]: https://github.com/sktime/sktime/tree/main/sktime/param_est\n\n\n## :hourglass_flowing_sand: Install sktime\nFor troubleshooting and detailed installation instructions, see the [documentation](https://www.sktime.net/en/latest/installation.html).\n\n- **Operating system**: macOS X · Linux · Windows 8.1 or higher\n- **Python version**: Python 3.9, 3.10, 3.11, 3.12, and 3.13 (only 64-bit)\n- **Package managers**: [pip] · [conda] (via `conda-forge`)\n\n[pip]: https://pip.pypa.io/en/stable/\n[conda]: https://docs.conda.io/en/latest/\n\n### pip\nUsing pip, sktime releases are available as source packages and binary wheels.\nAvailable wheels are listed [here](https://pypi.org/simple/sktime/).\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://github.com/conda-forge/sktime-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\u003e\u003e\u003e 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\u003e\u003e\u003e 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://github.com/all-contributors/all-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://www.sktime.net/en/latest/get_involved/contributing.html\n[donate]: https://opencollective.com/sktime\n[extension templates]: https://github.com/sktime/sktime/tree/main/extension_templates\n[developer guides]: https://www.sktime.net/en/latest/developer_guide.html\n[contributors]: https://github.com/sktime/sktime/blob/main/CONTRIBUTORS.md\n[governance]: https://www.sktime.net/en/latest/get_involved/governance.html\n[mentoring]: https://github.com/sktime/mentoring\n[meetings]: https://calendar.google.com/calendar/u/0/embed?src=sktime.toolbox@gmail.com\u0026ctz=UTC\n[enhancement proposals]: https://github.com/sktime/enhancement-proposals\n[roles]: https://www.sktime.net/en/latest/about/team.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://github.com/sktime/sktime/graphs/contributors\"\u003e\n\u003cimg src=\"https://opencollective.com/sktime/contributors.svg?width=600\u0026button=false\" /\u003e\n\u003c/a\u003e\n\u003cbr\u003e\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","funding_links":["https://github.com/sponsors/sktime","https://opencollective.com/sktime"],"categories":["Python","Others","Time Series","Time-Series \u0026 Financial","Curated List","⏳ Time Series Analysis","Table of Contents"],"sub_categories":["Others","Time Series Analysis","Tools","Workshops and Tutorials"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsktime%2Fsktime","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsktime%2Fsktime/lists"}