base on A unified framework for machine learning with time series ## Welcome to sktime <a href="https://www.sktime.net"><img src="https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.svg" width="175" align="right" /></a> > A unified interface for machine learning with time series :rocket: **Version 0.37.0 out now!** [Check out the release notes here](https://www.sktime.net/en/latest/changelog.html). sktime 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. [scikit-learn]: https://scikit-learn.org/stable/ | | **[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)** | |---|---| | **Open&#160;Source** | [](https://github.com/sktime/sktime/blob/main/LICENSE) [](https://gc-os-ai.github.io/) | | **Tutorials** | [](https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples) [](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) | | **Community** | [](https://discord.com/invite/54ACzaFsn7) [](https://www.linkedin.com/company/scikit-time/) | | **CI/CD** | [](https://github.com/sktime/sktime/actions/workflows/wheels.yml) [](https://www.sktime.net/en/latest/?badge=latest) [](https://github.com/sktime/sktime) | | **Code** | [](https://pypi.org/project/sktime/) [](https://anaconda.org/conda-forge/sktime) [](https://www.python.org/) [](https://github.com/psf/black) | | **Downloads** |   [)](https://pepy.tech/project/sktime) | | **Citation** | [](https://doi.org/10.5281/zenodo.3749000) | ## :books: Documentation | Documentation | | |--------------------------------------| -------------------------------------------------------------- | | :star: **[Tutorials]** | New to sktime? Here's everything you need to know! | | :clipboard: **[Binder Notebooks]** | Example notebooks to play with in your browser. | | :woman_technologist: **[Examples]** | How to use sktime and its features. | | :scissors: **[Extension Templates]** | How to build your own estimator using sktime's API. | | :control_knobs: **[API Reference]** | The detailed reference for sktime's API. | | :tv: **[Video Tutorial]** | Our video tutorial from 2021 PyData Global. | | :hammer_and_wrench: **[Changelog]** | Changes and version history. | | :deciduous_tree: **[Roadmap]** | sktime's software and community development plan. | | :pencil: **[Related Software]** | A list of related software. | [tutorials]: https://www.sktime.net/en/latest/tutorials.html [binder notebooks]: https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples [examples]: https://www.sktime.net/en/latest/examples.html [video tutorial]: https://github.com/sktime/sktime-tutorial-pydata-global-2021 [api reference]: https://www.sktime.net/en/latest/api_reference.html [changelog]: https://www.sktime.net/en/latest/changelog.html [roadmap]: https://www.sktime.net/en/latest/roadmap.html [related software]: https://www.sktime.net/en/latest/related_software.html ## :speech_balloon: Where to ask questions Questions 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. | Type | Platforms | | ------------------------------- | --------------------------------------- | | :bug: **Bug Reports** | [GitHub Issue Tracker] | | :sparkles: **Feature Requests & Ideas** | [GitHub Issue Tracker] | | :woman_technologist: **Usage Questions** | [GitHub Discussions] · [Stack Overflow] | | :speech_balloon: **General Discussion** | [GitHub Discussions] | | :factory: **Contribution & Development** | `dev-chat` channel · [Discord] | | :globe_with_meridians: **Meet-ups and collaboration sessions** | [Discord] - Fridays 13 UTC, dev/meet-ups channel | [github issue tracker]: https://github.com/sktime/sktime/issues [github discussions]: https://github.com/sktime/sktime/discussions [stack overflow]: https://stackoverflow.com/questions/tagged/sktime [discord]: https://discord.com/invite/54ACzaFsn7 ## :dizzy: Features Our 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. sktime also provides **interfaces to related libraries**, for example [scikit-learn], [statsmodels], [tsfresh], [PyOD], and [fbprophet], among others. [statsmodels]: https://www.statsmodels.org/stable/index.html [tsfresh]: https://tsfresh.readthedocs.io/en/latest/ [pyod]: https://pyod.readthedocs.io/en/latest/ [fbprophet]: https://facebook.github.io/prophet/ | Module | Status | Links | |---|---|---| | **[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) | | **[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) | | **[Time Series Regression]** | stable | [API Reference](https://www.sktime.net/en/latest/api_reference/regression.html) | | **[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) | | **[Detection tasks]** | maturing | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/detection.py) | | **[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) | | **[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) | | **[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) | | **[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) | | **[Time Series Splitters]** | maturing | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/split.py) | | | **[Distributions and simulation]** | experimental | | [forecasting]: https://github.com/sktime/sktime/tree/main/sktime/forecasting [time series classification]: https://github.com/sktime/sktime/tree/main/sktime/classification [time series regression]: https://github.com/sktime/sktime/tree/main/sktime/regression [time series clustering]: https://github.com/sktime/sktime/tree/main/sktime/clustering [detection tasks]: https://github.com/sktime/sktime/tree/main/sktime/detection [time series distances/kernels]: https://github.com/sktime/sktime/tree/main/sktime/dists_kernels [time series alignment]: https://github.com/sktime/sktime/tree/main/sktime/alignment [transformations]: https://github.com/sktime/sktime/tree/main/sktime/transformations [distributions and simulation]: https://github.com/sktime/sktime/tree/main/sktime/proba [time series splitters]: https://github.com/sktime/sktime/tree/main/sktime/split [parameter fitting]: https://github.com/sktime/sktime/tree/main/sktime/param_est ## :hourglass_flowing_sand: Install sktime For troubleshooting and detailed installation instructions, see the [documentation](https://www.sktime.net/en/latest/installation.html). - **Operating system**: macOS X · Linux · Windows 8.1 or higher - **Python version**: Python 3.8, 3.9, 3.10, 3.11, and 3.12 (only 64-bit) - **Package managers**: [pip] · [conda] (via `conda-forge`) [pip]: https://pip.pypa.io/en/stable/ [conda]: https://docs.conda.io/en/latest/ ### pip Using pip, sktime releases are available as source packages and binary wheels. Available wheels are listed [here](https://pypi.org/simple/sktime/). ```bash pip install sktime ``` or, with maximum dependencies, ```bash pip install sktime[all_extras] ``` For curated sets of soft dependencies for specific learning tasks: ```bash pip install sktime[forecasting] # for selected forecasting dependencies pip install sktime[forecasting,transformations] # forecasters and transformers ``` or similar. Valid sets are: * `forecasting` * `transformations` * `classification` * `regression` * `clustering` * `param_est` * `networks` * `detection` * `alignment` Cave: in general, not all soft dependencies for a learning task are installed, only a curated selection. ### conda You can also install sktime from `conda` via the `conda-forge` channel. The feedstock including the build recipe and configuration is maintained in [this conda-forge repository](https://github.com/conda-forge/sktime-feedstock). ```bash conda install -c conda-forge sktime ``` or, with maximum dependencies, ```bash conda install -c conda-forge sktime-all-extras ``` (as `conda` does not support dependency sets, flexible choice of soft dependencies is unavailable via `conda`) ## :zap: Quickstart ### Forecasting ``` python from sktime.datasets import load_airline from sktime.forecasting.base import ForecastingHorizon from sktime.forecasting.theta import ThetaForecaster from sktime.split import temporal_train_test_split from sktime.performance_metrics.forecasting import mean_absolute_percentage_error y = load_airline() y_train, y_test = temporal_train_test_split(y) fh = ForecastingHorizon(y_test.index, is_relative=False) forecaster = ThetaForecaster(sp=12) # monthly seasonal periodicity forecaster.fit(y_train) y_pred = forecaster.predict(fh) mean_absolute_percentage_error(y_test, y_pred) >>> 0.08661467738190656 ``` ### Time Series Classification ```python from sktime.classification.interval_based import TimeSeriesForestClassifier from sktime.datasets import load_arrow_head from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score X, y = load_arrow_head() X_train, X_test, y_train, y_test = train_test_split(X, y) classifier = TimeSeriesForestClassifier() classifier.fit(X_train, y_train) y_pred = classifier.predict(X_test) accuracy_score(y_test, y_pred) >>> 0.8679245283018868 ``` ## :wave: How to get involved There 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. | Documentation | | | -------------------------- | -------------------------------------------------------------- | | :gift_heart: **[Contribute]** | How to contribute to sktime. | | :school_satchel: **[Mentoring]** | New to open source? Apply to our mentoring program! | | :date: **[Meetings]** | Join our discussions, tutorials, workshops, and sprints! | | :woman_mechanic: **[Developer Guides]** | How to further develop sktime's code base. | | :construction: **[Enhancement Proposals]** | Design a new feature for sktime. | | :medal_sports: **[Contributors]** | A list of all contributors. | | :raising_hand: **[Roles]** | An overview of our core community roles. | | :money_with_wings: **[Donate]** | Fund sktime maintenance and development. | | :classical_building: **[Governance]** | How and by whom decisions are made in sktime's community. | [contribute]: https://www.sktime.net/en/latest/get_involved/contributing.html [donate]: https://opencollective.com/sktime [extension templates]: https://github.com/sktime/sktime/tree/main/extension_templates [developer guides]: https://www.sktime.net/en/latest/developer_guide.html [contributors]: https://github.com/sktime/sktime/blob/main/CONTRIBUTORS.md [governance]: https://www.sktime.net/en/latest/get_involved/governance.html [mentoring]: https://github.com/sktime/mentoring [meetings]: https://calendar.google.com/calendar/u/0/embed?src=sktime.toolbox@gmail.com&ctz=UTC [enhancement proposals]: https://github.com/sktime/enhancement-proposals [roles]: https://www.sktime.net/en/latest/about/team.html ## :trophy: Hall of fame Thanks to all our community for all your wonderful contributions, PRs, issues, ideas. <a href="https://github.com/sktime/sktime/graphs/contributors"> <img src="https://opencollective.com/sktime/contributors.svg?width=600&button=false" /> </a> <br> ## :bulb: Project vision * **By the community, for the community** -- developed by a friendly and collaborative community. * The **right tool for the right task** -- helping users to diagnose their learning problem and suitable scientific model types. * **Embedded in state-of-art ecosystems** and **provider of interoperable interfaces** -- interoperable with [scikit-learn], [statsmodels], [tsfresh], and other community favorites. * **Rich model composition and reduction functionality** -- build tuning and feature extraction pipelines, solve forecasting tasks with [scikit-learn] regressors. * **Clean, descriptive specification syntax** -- based on modern object-oriented design principles for data science. * **Fair model assessment and benchmarking** -- build your models, inspect your models, check your models, and avoid pitfalls. * **Easily extensible** -- easy extension templates to add your own algorithms compatible with sktime's API. ", Assign "at most 3 tags" to the expected json: {"id":"2614","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"