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https://github.com/sktime/sktime

A unified framework for machine learning with time series
https://github.com/sktime/sktime

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

Last synced: 5 days ago
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A unified framework for machine learning with time series

Host: GitHub
URL: https://github.com/sktime/sktime
Owner: sktime
License: bsd-3-clause
Created: 2018-11-06T15:08:24.000Z (about 6 years ago)
Default Branch: main
Last Pushed: 2024-12-05T23:15:32.000Z (16 days ago)
Last Synced: 2024-12-06T21:21:45.593Z (15 days ago)
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
Language: Python
Homepage: https://www.sktime.net
Size: 75.5 MB
Stars: 8,007
Watchers: 108
Forks: 1,389
Open Issues: 1,246
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS
- Governance: GOVERNANCE.md

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Awesome-time-series - 📚 SKTIME - | ⭐️ 6.1K | sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. (Others)
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README

        
## Welcome to sktime



> A unified interface for machine learning with time series

:rocket: **Version 0.35.0 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 Source** | [![BSD 3-clause](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/sktime/sktime/blob/main/LICENSE) |

| **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&label=YouTube&message=tutorials&color=red)](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |

| **Community** | [![!discord](https://img.shields.io/static/v1?logo=discord&label=discord&message=chat&color=lightgreen)](https://discord.com/invite/54ACzaFsn7) [![!slack](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/scikit-time/)  |

| **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) |

| **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)  |

| **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&units=international_system&left_color=grey&right_color=blue&left_text=cumulative%20(pypi))](https://pepy.tech/project/sktime) |

| **Citation** | [![!zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.3749000.svg)](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/[email protected]&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.










## :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.