# ⌛ Welcome to aeon `aeon` is an open-source toolkit for time series machine learning. Fully compatible with [scikit-learn](https://scikit-learn.org), it brings together the latest machine learning methods alongside a wide range of classical approaches for tasks such as forecasting, clustering, and classification. Our goal is to provide a comprehensive collection of state-of-the-art time series algorithms, with efficient implementations powered by `numba`, and to promote reproducible research in the field of time series machine learning. The latest `aeon` release is `v1.3.0`. 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` - `transformations.collection.self_supervised` - `transformations.collection.imbalance` | 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://scorecard.dev/viewer/?uri=github.com/aeon-toolkit/aeon) | | **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://discord.gg/D6rzqHGKRJ) [](https://www.linkedin.com/company/aeon-toolkit/) [](https://twitter.com/aeon_toolkit) [](https://medium.com/@aeon.toolkit) | | **Affiliation** | [](https://numfocus.org/sponsored-projects/affiliated-projects) | ## ⚙️ Installation `aeon` requires a Python version of 3.10 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] & [Discord] | | 💻 **Usage Questions** | [GitHub Discussions] & [Discord] | | 💬 **General Discussion** | [GitHub Discussions] & [Discord] | | 🏭 **Contribution & Development** | [Discord] | [GitHub Issue Tracker]: https://github.com/aeon-toolkit/aeon/issues [GitHub Discussions]: https://github.com/aeon-toolkit/aeon/discussions [Discord]: https://discord.gg/D6rzqHGKRJ For enquiries about the project or collaboration, our email is [contact@aeon-toolkit.org](mailto:contact@aeon-toolkit.org). ## 🔨 Contributing to aeon If you are interested in contributing to `aeon`, please see our [contributing guide](https://www.aeon-toolkit.org/en/latest/contributing.html) and have a read through before assigning an issue and creating a pull request. Be aware that the `latest` version of the docs is the development version, and the `stable` version is the latest release. The `aeon` developers are volunteers so please be patient with responses to comments and pull request reviews. If you have any questions, feel free to ask using the above mediums. ## 📚 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). ## 👥 Further information `aeon` was forked from `sktime` `v0.16.0` in 2022 by an initial group of eight core developers. You can read more about the project's history and governance structure in our [About Us page](https://www.aeon-toolkit.org/en/stable/about.html).