# ⌛ 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 `v0.11.1`. 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`, `benchmarking`, `segmentation`, `similarity_search`, `testing`, `transformations/series`, `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) | ## ⚙️ 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 forecasting, classification and clustering. ### Classification *It's worth mentioning that the classifier used in the example can easily be swapped out for a regressor, and the labels for numeric targets. This flexibility allowing for seamless adaptation to different tasks and datasets while preserving API consistency.* ```python import numpy as np from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier X = [[[1, 2, 3, 4, 5, 5]], # 3D array example (univariate) [[1, 2, 3, 4, 4, 2]], # Three samples, one channel, six series length, [[8, 7, 6, 5, 4, 4]]] y = ['low', 'low', 'high'] # class labels for each sample X = np.array(X) y = np.array(y) 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 ```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, six series length, [[8, 7, 6, 5, 4, 4]]]) 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