aeon is an open-source toolkit for learning from time series. It is compatible with scikit-learn 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.

Our webpage and documentation is available at https://aeon-toolkit.org.

The following modules are still considered experimental, and the deprecation policy does not apply:

• anomaly_detection
• forecasting
• segmentation
• similarity_search
• visualisation

Overview
CI/CD
Code
Community
Affiliation

aeon requires a Python version of 3.9 or greater. Our full installation guide is available in our documentation.

The easiest way to install aeon is via pip:

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:

pip install aeon[all_extras]

Instructions for installation from the GitHub source can be found here.

The best place to get started for all aeon packages is our getting started guide.

Below we provide a quick example of how to use aeon for classification and clustering.

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.

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']

Time series clustering groups similar time series together from a collection of time series.

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])

For enquiries about the project or collaboration, our email is contact@aeon-toolkit.org.

If you use aeon we would appreciate a citation of the following paper:

@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.

aeon was forked from sktime v0.16.0 in 2022 by an initial group of eight core developers.