The University of Southampton
University of Southampton Institutional Repository

aeon: a Python toolkit for learning from time series

aeon: a Python toolkit for learning from time series
aeon: a Python toolkit for learning from time series
aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regression and clustering, as well as a variety of utilities, transformations and distance measures designed for time series data. aeon also has a number of experimental modules for tasks such as anomaly detection, similarity search and segmentation. aeon follows the scikit-learn API as much as possible to help new users and enable easy integration of aeon estimators with useful tools such as model selection and pipelines. It provides a broad library of time series algorithms, including efficient implementations of the very latest advances in research. Using a system of optional dependencies, aeon integrates a wide variety of packages into a single interface while keeping the core framework with minimal dependencies. The package is distributed under the 3-Clause BSD license and is available at https://github.com/aeon-toolkit/aeon.
cs.LG
1532-4435
Middlehurst, Matthew
44ae267d-b9ec-42b2-b818-d901b221daf9
Ismail-Fawaz, Ali
3814ceb3-f8fa-475e-955e-d477a28da875
Guillaume, Antoine
77f622b5-b790-499b-b0c9-c38be167129b
Holder, Christopher
fb345cc6-00fa-4256-80ba-a8d3cbdb768b
Rubio, David Guijo
eec9f755-1750-4137-b034-20ada25822c5
Bulatova, Guzal
243e4f3b-c1d4-4486-b278-52e41c75f7c5
Tsaprounis, Leonidas
4eb4760e-0299-492e-8434-7851614529d7
Mentel, Lukasz
862d2f30-21db-444a-9ac0-fda6a0bc56d9
Walter, Martin
b57092fc-ae2d-4f3c-ab93-12b21f07a392
Schäfer, Patrick
7575442d-65cd-4333-963e-4e0cd8c0d34d
Bagnall, Anthony
d31e6506-2a00-4358-ba3f-baefd48d59d8
Middlehurst, Matthew
44ae267d-b9ec-42b2-b818-d901b221daf9
Ismail-Fawaz, Ali
3814ceb3-f8fa-475e-955e-d477a28da875
Guillaume, Antoine
77f622b5-b790-499b-b0c9-c38be167129b
Holder, Christopher
fb345cc6-00fa-4256-80ba-a8d3cbdb768b
Rubio, David Guijo
eec9f755-1750-4137-b034-20ada25822c5
Bulatova, Guzal
243e4f3b-c1d4-4486-b278-52e41c75f7c5
Tsaprounis, Leonidas
4eb4760e-0299-492e-8434-7851614529d7
Mentel, Lukasz
862d2f30-21db-444a-9ac0-fda6a0bc56d9
Walter, Martin
b57092fc-ae2d-4f3c-ab93-12b21f07a392
Schäfer, Patrick
7575442d-65cd-4333-963e-4e0cd8c0d34d
Bagnall, Anthony
d31e6506-2a00-4358-ba3f-baefd48d59d8

Middlehurst, Matthew, Ismail-Fawaz, Ali, Guillaume, Antoine, Holder, Christopher, Rubio, David Guijo, Bulatova, Guzal, Tsaprounis, Leonidas, Mentel, Lukasz, Walter, Martin, Schäfer, Patrick and Bagnall, Anthony (2024) aeon: a Python toolkit for learning from time series. Journal of Machine Learning Research, 25, [289].

Record type: Article

Abstract

aeon is a unified Python 3 library for all machine learning tasks involving time series. The package contains modules for time series forecasting, classification, extrinsic regression and clustering, as well as a variety of utilities, transformations and distance measures designed for time series data. aeon also has a number of experimental modules for tasks such as anomaly detection, similarity search and segmentation. aeon follows the scikit-learn API as much as possible to help new users and enable easy integration of aeon estimators with useful tools such as model selection and pipelines. It provides a broad library of time series algorithms, including efficient implementations of the very latest advances in research. Using a system of optional dependencies, aeon integrates a wide variety of packages into a single interface while keeping the core framework with minimal dependencies. The package is distributed under the 3-Clause BSD license and is available at https://github.com/aeon-toolkit/aeon.

Text
aeon JMLR - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (766kB)
Text
23-1444 - Version of Record
Available under License Creative Commons Attribution.
Download (766kB)

More information

Published date: 1 September 2024
Keywords: cs.LG

Identifiers

Local EPrints ID: 500463
URI: http://eprints.soton.ac.uk/id/eprint/500463
ISSN: 1532-4435
PURE UUID: b8434434-3e9e-4e95-9ece-565b3c731a0d
ORCID for Matthew Middlehurst: ORCID iD orcid.org/0000-0002-3293-8779
ORCID for Anthony Bagnall: ORCID iD orcid.org/0000-0003-2360-8994

Catalogue record

Date deposited: 30 Apr 2025 16:56
Last modified: 22 Aug 2025 02:41

Export record

Contributors

Author: Matthew Middlehurst ORCID iD
Author: Ali Ismail-Fawaz
Author: Antoine Guillaume
Author: Christopher Holder
Author: David Guijo Rubio
Author: Guzal Bulatova
Author: Leonidas Tsaprounis
Author: Lukasz Mentel
Author: Martin Walter
Author: Patrick Schäfer
Author: Anthony Bagnall ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×