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
Middlehurst, Matthew
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Ismail-Fawaz, Ali
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Guillaume, Antoine
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Holder, Christopher
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Rubio, David Guijo
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Bulatova, Guzal
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Tsaprounis, Leonidas
4eb4760e-0299-492e-8434-7851614529d7
Mentel, Lukasz
862d2f30-21db-444a-9ac0-fda6a0bc56d9
Walter, Martin
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Schäfer, Patrick
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Bagnall, Anthony
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1 September 2024
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].
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.
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aeon JMLR
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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
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Date deposited: 30 Apr 2025 16:56
Last modified: 22 Aug 2025 02:41
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Contributors
Author:
Matthew Middlehurst
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
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