| Name | aeon JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | A toolkit for machine learning from time series |
| upload_time | 2024-11-28 17:25:52 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.9 |
| license | BSD 3-Clause License Copyright (c) The aeon developers. Copyright (c) 2022 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. |
| keywords |
data-science
machine-learning
data-mining
time-series
scikit-learn
forecasting
time-series-analysis
time-series-classification
time-series-clustering
time-series-regression
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
|
<p align="center">
<a href="https://aeon-toolkit.org"><img src="https://raw.githubusercontent.com/aeon-toolkit/aeon/main/docs/images/logo/aeon-logo-blue-compact.png" width="50%" alt="aeon logo" /></a>
</p>
# ⌛ Welcome to aeon
`aeon` is an open-source toolkit for learning from time series. It is compatible with
[scikit-learn](https://scikit-learn.org) and provides access to the very latest
algorithms for time series machine learning, in addition to a range of classical
techniques for learning tasks such as forecasting and classification.
We strive to provide a broad library of time series algorithms including the
latest advances, offer efficient implementations using numba, and interfaces with other
time series packages to provide a single framework for algorithm comparison.
The latest `aeon` release is `v1.0.0`. You can view the full changelog
[here](https://www.aeon-toolkit.org/en/stable/changelog.html).
Our webpage and documentation is available at https://aeon-toolkit.org.
The following modules are still considered experimental, and the [deprecation policy](https://www.aeon-toolkit.org/en/stable/developer_guide/deprecation.html)
does not apply:
- `anomaly_detection`
- `forecasting`
- `segmentation`
- `similarity_search`
- `visualisation`
| Overview | |
|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **CI/CD** | [](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [](https://www.aeon-toolkit.org/en/stable/) [](https://www.aeon-toolkit.org/en/latest/) [](https://codecov.io/gh/aeon-toolkit/aeon) [](https://img.shields.io/ossf-scorecard/github.com/aeon-toolkit/aeon?label=openssf%20scorecard&style=flat) |
| **Code** | [](https://pypi.org/project/aeon/) [](https://anaconda.org/conda-forge/aeon) [](https://www.python.org/) [](https://github.com/psf/black) [](https://github.com/aeon-toolkit/aeon/blob/main/LICENSE) [](https://mybinder.org/v2/gh/aeon-toolkit/aeon/main?filepath=examples) |
| **Community** | [](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) [](https://www.linkedin.com/company/aeon-toolkit/) [](https://twitter.com/aeon_toolkit) |
| **Affiliation** | [](https://numfocus.org/sponsored-projects/affiliated-projects) |
## ⚙️ Installation
`aeon` requires a Python version of 3.9 or greater. Our full installation guide is
available in our [documentation](https://www.aeon-toolkit.org/en/stable/installation.html).
The easiest way to install `aeon` is via pip:
```bash
pip install aeon
```
Some estimators require additional packages to be installed. If you want to install
the full package with all optional dependencies, you can use:
```bash
pip install aeon[all_extras]
```
Instructions for installation from the [GitHub source](https://github.com/aeon-toolkit/aeon)
can be found [here](https://www.aeon-toolkit.org/en/stable/developer_guide/dev_installation.html).
## ⏲️ Getting started
The best place to get started for all `aeon` packages is our [getting started guide](https://www.aeon-toolkit.org/en/stable/getting_started.html).
Below we provide a quick example of how to use `aeon` for classification and clustering.
### Classification/Regression
Time series classification looks to predict class labels fore unseen series using a
model fitted from a collection of time series. The framework for regression is similar,
replace the classifier with a regressor and the labels with continuous values.
```python
import numpy as np
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
X = np.array([[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)
[[1, 2, 3, 4, 4, 2]], # Three samples, one channel,
[[8, 7, 6, 5, 4, 4]]]) # six series length
y = np.array(['low', 'low', 'high']) # class labels for each sample
clf = KNeighborsTimeSeriesClassifier(distance="dtw")
clf.fit(X, y) # fit the classifier on train data
>>> KNeighborsTimeSeriesClassifier()
X_test = np.array(
[[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
y_pred = clf.predict(X_test) # make class predictions on new data
>>> ['low' 'high' 'high']
```
### Clustering
Time series clustering groups similar time series together from a collection of
time series.
```python
import numpy as np
from aeon.clustering import TimeSeriesKMeans
X = np.array([[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)
[[1, 2, 3, 4, 4, 2]], # Three samples, one channel,
[[8, 7, 6, 5, 4, 4]]]) # six series length
clu = TimeSeriesKMeans(distance="dtw", n_clusters=2)
clu.fit(X) # fit the clusterer on train data
>>> TimeSeriesKMeans(distance='dtw', n_clusters=2)
clu.labels_ # get training cluster labels
>>> array([0, 0, 1])
X_test = np.array(
[[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]
)
clu.predict(X_test) # Assign clusters to new data
>>> array([1, 0, 0])
```
## 💬 Where to ask questions
| Type | Platforms |
|------------------------------------|-----------------------------------|
| 🐛 **Bug Reports** | [GitHub Issue Tracker] |
| ✨ **Feature Requests & Ideas** | [GitHub Issue Tracker] & [Slack] |
| 💻 **Usage Questions** | [GitHub Discussions] & [Slack] |
| 💬 **General Discussion** | [GitHub Discussions] & [Slack] |
| 🏭 **Contribution & Development** | [Slack] |
[GitHub Issue Tracker]: https://github.com/aeon-toolkit/aeon/issues
[GitHub Discussions]: https://github.com/aeon-toolkit/aeon/discussions
[Slack]: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA
For enquiries about the project or collaboration, our email is
[contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org).
## 📚 Citation
If you use `aeon` we would appreciate a citation of the following [paper](https://jmlr.org/papers/v25/23-1444.html):
```bibtex
@article{aeon24jmlr,
author = {Matthew Middlehurst and Ali Ismail-Fawaz and Antoine Guillaume and Christopher Holder and David Guijo-Rubio and Guzal Bulatova and Leonidas Tsaprounis and Lukasz Mentel and Martin Walter and Patrick Sch{{\"a}}fer and Anthony Bagnall},
title = {aeon: a Python Toolkit for Learning from Time Series},
journal = {Journal of Machine Learning Research},
year = {2024},
volume = {25},
number = {289},
pages = {1--10},
url = {http://jmlr.org/papers/v25/23-1444.html}
}
```
If you let us know about your paper using `aeon`, we will happily list it [here](https://www.aeon-toolkit.org/en/stable/papers_using_aeon.html).
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"description": "<p align=\"center\">\n <a href=\"https://aeon-toolkit.org\"><img src=\"https://raw.githubusercontent.com/aeon-toolkit/aeon/main/docs/images/logo/aeon-logo-blue-compact.png\" width=\"50%\" alt=\"aeon logo\" /></a>\n</p>\n\n# \u231b Welcome to aeon\n\n`aeon` is an open-source toolkit for learning from time series. It is compatible with\n[scikit-learn](https://scikit-learn.org) and provides access to the very latest\nalgorithms for time series machine learning, in addition to a range of classical\ntechniques for learning tasks such as forecasting and classification.\n\nWe strive to provide a broad library of time series algorithms including the\nlatest advances, offer efficient implementations using numba, and interfaces with other\ntime series packages to provide a single framework for algorithm comparison.\n\nThe latest `aeon` release is `v1.0.0`. You can view the full changelog\n[here](https://www.aeon-toolkit.org/en/stable/changelog.html).\n\nOur webpage and documentation is available at https://aeon-toolkit.org.\n\nThe following modules are still considered experimental, and the [deprecation policy](https://www.aeon-toolkit.org/en/stable/developer_guide/deprecation.html)\ndoes not apply:\n\n- `anomaly_detection`\n- `forecasting`\n- `segmentation`\n- `similarity_search`\n- `visualisation`\n\n| Overview | |\n|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| **CI/CD** | [](https://github.com/aeon-toolkit/aeon/actions/workflows/release.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/pr_pytest.yml) [](https://github.com/aeon-toolkit/aeon/actions/workflows/periodic_tests.yml) [](https://www.aeon-toolkit.org/en/stable/) [](https://www.aeon-toolkit.org/en/latest/) [](https://codecov.io/gh/aeon-toolkit/aeon) [](https://img.shields.io/ossf-scorecard/github.com/aeon-toolkit/aeon?label=openssf%20scorecard&style=flat) |\n| **Code** | [](https://pypi.org/project/aeon/) [](https://anaconda.org/conda-forge/aeon) [](https://www.python.org/) [](https://github.com/psf/black) [](https://github.com/aeon-toolkit/aeon/blob/main/LICENSE) [](https://mybinder.org/v2/gh/aeon-toolkit/aeon/main?filepath=examples) |\n| **Community** | [](https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA) [](https://www.linkedin.com/company/aeon-toolkit/) [](https://twitter.com/aeon_toolkit) |\n| **Affiliation** | [](https://numfocus.org/sponsored-projects/affiliated-projects) |\n\n## \u2699\ufe0f Installation\n\n`aeon` requires a Python version of 3.9 or greater. Our full installation guide is\navailable in our [documentation](https://www.aeon-toolkit.org/en/stable/installation.html).\n\nThe easiest way to install `aeon` is via pip:\n\n```bash\npip install aeon\n```\n\nSome estimators require additional packages to be installed. If you want to install\nthe full package with all optional dependencies, you can use:\n\n```bash\npip install aeon[all_extras]\n```\n\nInstructions for installation from the [GitHub source](https://github.com/aeon-toolkit/aeon)\ncan be found [here](https://www.aeon-toolkit.org/en/stable/developer_guide/dev_installation.html).\n\n## \u23f2\ufe0f Getting started\n\nThe best place to get started for all `aeon` packages is our [getting started guide](https://www.aeon-toolkit.org/en/stable/getting_started.html).\n\nBelow we provide a quick example of how to use `aeon` for classification and clustering.\n\n### Classification/Regression\n\nTime series classification looks to predict class labels fore unseen series using a\nmodel fitted from a collection of time series. The framework for regression is similar,\nreplace the classifier with a regressor and the labels with continuous values.\n\n```python\nimport numpy as np\nfrom aeon.classification.distance_based import KNeighborsTimeSeriesClassifier\n\nX = np.array([[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)\n [[1, 2, 3, 4, 4, 2]], # Three samples, one channel,\n [[8, 7, 6, 5, 4, 4]]]) # six series length\ny = np.array(['low', 'low', 'high']) # class labels for each sample\n\nclf = KNeighborsTimeSeriesClassifier(distance=\"dtw\")\nclf.fit(X, y) # fit the classifier on train data\n>>> KNeighborsTimeSeriesClassifier()\n\nX_test = np.array(\n [[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]\n)\ny_pred = clf.predict(X_test) # make class predictions on new data\n>>> ['low' 'high' 'high']\n```\n\n### Clustering\n\nTime series clustering groups similar time series together from a collection of\ntime series.\n\n```python\nimport numpy as np\nfrom aeon.clustering import TimeSeriesKMeans\n\nX = np.array([[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate)\n [[1, 2, 3, 4, 4, 2]], # Three samples, one channel,\n [[8, 7, 6, 5, 4, 4]]]) # six series length\n\nclu = TimeSeriesKMeans(distance=\"dtw\", n_clusters=2)\nclu.fit(X) # fit the clusterer on train data\n>>> TimeSeriesKMeans(distance='dtw', n_clusters=2)\n\nclu.labels_ # get training cluster labels\n>>> array([0, 0, 1])\n\nX_test = np.array(\n [[[2, 2, 2, 2, 2, 2]], [[5, 5, 5, 5, 5, 5]], [[6, 6, 6, 6, 6, 6]]]\n)\nclu.predict(X_test) # Assign clusters to new data\n>>> array([1, 0, 0])\n```\n\n## \ud83d\udcac Where to ask questions\n\n| Type | Platforms |\n|------------------------------------|-----------------------------------|\n| \ud83d\udc1b **Bug Reports** | [GitHub Issue Tracker] |\n| \u2728 **Feature Requests & Ideas** | [GitHub Issue Tracker] & [Slack] |\n| \ud83d\udcbb **Usage Questions** | [GitHub Discussions] & [Slack] |\n| \ud83d\udcac **General Discussion** | [GitHub Discussions] & [Slack] |\n| \ud83c\udfed **Contribution & Development** | [Slack] |\n\n[GitHub Issue Tracker]: https://github.com/aeon-toolkit/aeon/issues\n[GitHub Discussions]: https://github.com/aeon-toolkit/aeon/discussions\n[Slack]: https://join.slack.com/t/aeon-toolkit/shared_invite/zt-22vwvut29-HDpCu~7VBUozyfL_8j3dLA\n\nFor enquiries about the project or collaboration, our email is\n[contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org).\n\n## \ud83d\udcda Citation\n\nIf you use `aeon` we would appreciate a citation of the following [paper](https://jmlr.org/papers/v25/23-1444.html):\n\n```bibtex\n@article{aeon24jmlr,\n author = {Matthew Middlehurst and Ali Ismail-Fawaz and Antoine Guillaume and Christopher Holder and David Guijo-Rubio and Guzal Bulatova and Leonidas Tsaprounis and Lukasz Mentel and Martin Walter and Patrick Sch{{\\\"a}}fer and Anthony Bagnall},\n title = {aeon: a Python Toolkit for Learning from Time Series},\n journal = {Journal of Machine Learning Research},\n year = {2024},\n volume = {25},\n number = {289},\n pages = {1--10},\n url = {http://jmlr.org/papers/v25/23-1444.html}\n}\n```\n\nIf you let us know about your paper using `aeon`, we will happily list it [here](https://www.aeon-toolkit.org/en/stable/papers_using_aeon.html).\n",
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