%global _empty_manifest_terminate_build 0
Name:		python-sktime
Version:	0.17.0
Release:	1
Summary:	A unified framework for machine learning with time series
License:	BSD 3-Clause License  Copyright (c) 2019 - 2020 The sktime developers.  All rights reserved.  Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:  * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.  * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.  * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 
URL:		https://www.sktime.net
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/42/6e/c7e23c65b469f8ee7a6f8ea735189f40bd2909f6cc41a61ab2eabd2b4f78/sktime-0.17.0.tar.gz
BuildArch:	noarch

Requires:	python3-deprecated
Requires:	python3-numpy
Requires:	python3-pandas
Requires:	python3-scikit-learn
Requires:	python3-scipy
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Requires:	python3-tensorflow
Requires:	python3-tensorflow-probability
Requires:	python3-jupyter
Requires:	python3-myst-parser
Requires:	python3-nbsphinx
Requires:	python3-numpydoc
Requires:	python3-pydata-sphinx-theme
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%description
<a href="https://www.sktime.net"><img src="https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.jpg?raw=true)" width="175" align="right" /></a>

# Welcome to sktime

> A unified interface for machine learning with time series

:rocket: **Version 0.17.0 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 time series classification, regression, clustering, annotation and forecasting. 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/

| Overview | |
|---|---|
| **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) [![!codecov](https://img.shields.io/codecov/c/github/sktime/sktime?label=codecov&logo=codecov)](https://codecov.io/gh/sktime/sktime) [![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) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples) |
| **Downloads**| [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=week&units=international_system&left_color=grey&right_color=blue&left_text=weekly%20(pypi))](https://pepy.tech/project/sktime) [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=month&units=international_system&left_color=grey&right_color=blue&left_text=monthly%20(pypi))](https://pepy.tech/project/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) |
| **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/) [![!twitter](https://img.shields.io/static/v1?logo=twitter&label=Twitter&message=news&color=lightblue)](https://twitter.com/sktime_toolbox) [![!youtube](https://img.shields.io/static/v1?logo=youtube&label=YouTube&message=tutorials&color=red)](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |
| **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: **[User Guides]**      | 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
[user guides]: https://www.sktime.net/en/latest/user_guide.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! Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can 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** | contributors channel · [Discord] |
| :globe_with_meridians: **Community collaboration session** | [Discord] - Fridays 3pm 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 aim is to make the time series analysis ecosystem more interoperable and usable as a whole. 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__ including pipelining, ensembling, tuning and reduction that enables users to apply an algorithm for one task to another.

sktime also provides **interfaces to related libraries**, for example [scikit-learn], [statsmodels], [tsfresh], [PyOD] and [fbprophet], among others.

For **deep learning**, see our companion package: [sktime-dl](https://github.com/sktime/sktime-dl).

[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)  |
| **[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) |
| **[Annotation]** | experimental | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/annotation.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
[annotation]: https://github.com/sktime/sktime/tree/main/sktime/annotation
[time series distances/kernels]: https://github.com/sktime/sktime/tree/main/sktime/dists_kernels
[transformations]: https://github.com/sktime/sktime/tree/main/sktime/transformations
[distributions and simulation]: https://github.com/sktime/sktime/tree/main/sktime/proba
[parameter fitting]: https://github.com/sktime/sktime/tree/main/sktime/param_est


## :hourglass_flowing_sand: Install sktime
For trouble shooting 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 3.7, 3.8, 3.9, 3.10, and 3.11 (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. You can see all available wheels [here](https://pypi.org/simple/sktime/).

```bash
pip install sktime
```

or, with maximum dependencies,

```bash
pip install sktime[all_extras]
```

### conda
You can also install sktime from `conda` via the `conda-forge` channel. For the feedstock including the build recipe and configuration, check out [this 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
```

## :zap: Quickstart

### Forecasting

```python
from sktime.datasets import load_airline
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.theta import ThetaForecaster
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/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

## :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 favourites.
* **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, avoid pitfalls.
* **easily extensible** -- easy extension templates to add your own algorithms compatible with sktime's API.


%package -n python3-sktime
Summary:	A unified framework for machine learning with time series
Provides:	python-sktime
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-sktime
<a href="https://www.sktime.net"><img src="https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.jpg?raw=true)" width="175" align="right" /></a>

# Welcome to sktime

> A unified interface for machine learning with time series

:rocket: **Version 0.17.0 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 time series classification, regression, clustering, annotation and forecasting. 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/

| Overview | |
|---|---|
| **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) [![!codecov](https://img.shields.io/codecov/c/github/sktime/sktime?label=codecov&logo=codecov)](https://codecov.io/gh/sktime/sktime) [![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) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples) |
| **Downloads**| [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=week&units=international_system&left_color=grey&right_color=blue&left_text=weekly%20(pypi))](https://pepy.tech/project/sktime) [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=month&units=international_system&left_color=grey&right_color=blue&left_text=monthly%20(pypi))](https://pepy.tech/project/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) |
| **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/) [![!twitter](https://img.shields.io/static/v1?logo=twitter&label=Twitter&message=news&color=lightblue)](https://twitter.com/sktime_toolbox) [![!youtube](https://img.shields.io/static/v1?logo=youtube&label=YouTube&message=tutorials&color=red)](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |
| **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: **[User Guides]**      | 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
[user guides]: https://www.sktime.net/en/latest/user_guide.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! Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can 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** | contributors channel · [Discord] |
| :globe_with_meridians: **Community collaboration session** | [Discord] - Fridays 3pm 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 aim is to make the time series analysis ecosystem more interoperable and usable as a whole. 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__ including pipelining, ensembling, tuning and reduction that enables users to apply an algorithm for one task to another.

sktime also provides **interfaces to related libraries**, for example [scikit-learn], [statsmodels], [tsfresh], [PyOD] and [fbprophet], among others.

For **deep learning**, see our companion package: [sktime-dl](https://github.com/sktime/sktime-dl).

[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)  |
| **[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) |
| **[Annotation]** | experimental | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/annotation.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
[annotation]: https://github.com/sktime/sktime/tree/main/sktime/annotation
[time series distances/kernels]: https://github.com/sktime/sktime/tree/main/sktime/dists_kernels
[transformations]: https://github.com/sktime/sktime/tree/main/sktime/transformations
[distributions and simulation]: https://github.com/sktime/sktime/tree/main/sktime/proba
[parameter fitting]: https://github.com/sktime/sktime/tree/main/sktime/param_est


## :hourglass_flowing_sand: Install sktime
For trouble shooting 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 3.7, 3.8, 3.9, 3.10, and 3.11 (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. You can see all available wheels [here](https://pypi.org/simple/sktime/).

```bash
pip install sktime
```

or, with maximum dependencies,

```bash
pip install sktime[all_extras]
```

### conda
You can also install sktime from `conda` via the `conda-forge` channel. For the feedstock including the build recipe and configuration, check out [this 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
```

## :zap: Quickstart

### Forecasting

```python
from sktime.datasets import load_airline
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.theta import ThetaForecaster
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/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

## :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 favourites.
* **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, avoid pitfalls.
* **easily extensible** -- easy extension templates to add your own algorithms compatible with sktime's API.


%package help
Summary:	Development documents and examples for sktime
Provides:	python3-sktime-doc
%description help
<a href="https://www.sktime.net"><img src="https://github.com/sktime/sktime/blob/main/docs/source/images/sktime-logo.jpg?raw=true)" width="175" align="right" /></a>

# Welcome to sktime

> A unified interface for machine learning with time series

:rocket: **Version 0.17.0 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 time series classification, regression, clustering, annotation and forecasting. 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/

| Overview | |
|---|---|
| **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) [![!codecov](https://img.shields.io/codecov/c/github/sktime/sktime?label=codecov&logo=codecov)](https://codecov.io/gh/sktime/sktime) [![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) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/sktime/sktime/main?filepath=examples) |
| **Downloads**| [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=week&units=international_system&left_color=grey&right_color=blue&left_text=weekly%20(pypi))](https://pepy.tech/project/sktime) [![Downloads](https://static.pepy.tech/personalized-badge/sktime?period=month&units=international_system&left_color=grey&right_color=blue&left_text=monthly%20(pypi))](https://pepy.tech/project/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) |
| **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/) [![!twitter](https://img.shields.io/static/v1?logo=twitter&label=Twitter&message=news&color=lightblue)](https://twitter.com/sktime_toolbox) [![!youtube](https://img.shields.io/static/v1?logo=youtube&label=YouTube&message=tutorials&color=red)](https://www.youtube.com/playlist?list=PLKs3UgGjlWHqNzu0LEOeLKvnjvvest2d0) |
| **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: **[User Guides]**      | 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
[user guides]: https://www.sktime.net/en/latest/user_guide.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! Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's shared publicly, so that more people can 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** | contributors channel · [Discord] |
| :globe_with_meridians: **Community collaboration session** | [Discord] - Fridays 3pm 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 aim is to make the time series analysis ecosystem more interoperable and usable as a whole. 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__ including pipelining, ensembling, tuning and reduction that enables users to apply an algorithm for one task to another.

sktime also provides **interfaces to related libraries**, for example [scikit-learn], [statsmodels], [tsfresh], [PyOD] and [fbprophet], among others.

For **deep learning**, see our companion package: [sktime-dl](https://github.com/sktime/sktime-dl).

[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)  |
| **[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) |
| **[Annotation]** | experimental | [Extension Template](https://github.com/sktime/sktime/blob/main/extension_templates/annotation.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
[annotation]: https://github.com/sktime/sktime/tree/main/sktime/annotation
[time series distances/kernels]: https://github.com/sktime/sktime/tree/main/sktime/dists_kernels
[transformations]: https://github.com/sktime/sktime/tree/main/sktime/transformations
[distributions and simulation]: https://github.com/sktime/sktime/tree/main/sktime/proba
[parameter fitting]: https://github.com/sktime/sktime/tree/main/sktime/param_est


## :hourglass_flowing_sand: Install sktime
For trouble shooting 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 3.7, 3.8, 3.9, 3.10, and 3.11 (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. You can see all available wheels [here](https://pypi.org/simple/sktime/).

```bash
pip install sktime
```

or, with maximum dependencies,

```bash
pip install sktime[all_extras]
```

### conda
You can also install sktime from `conda` via the `conda-forge` channel. For the feedstock including the build recipe and configuration, check out [this 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
```

## :zap: Quickstart

### Forecasting

```python
from sktime.datasets import load_airline
from sktime.forecasting.base import ForecastingHorizon
from sktime.forecasting.model_selection import temporal_train_test_split
from sktime.forecasting.theta import ThetaForecaster
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/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

## :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 favourites.
* **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, avoid pitfalls.
* **easily extensible** -- easy extension templates to add your own algorithms compatible with sktime's API.


%prep
%autosetup -n sktime-0.17.0

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-sktime -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.17.0-1
- Package Spec generated