Time series machine learning with aeon: classification and regression
Time series machine learning with aeon: classification and regression
We present the classification and regression modules of aeon, a Python library for all machine learning tasks involving time series. aeon follows the scikit-learn API and is compatible with its utilities such as model selection and pipelines. The toolkit contains a wide range of algorithms, including the state-of-the-art and popular benchmarks for each time series learning task. We demonstrate how to use the aeon toolkit for these tasks and give an example of where these algorithms may be useful. More information and an introductory video of the toolkit modules are available on the demo webpage https://aeon-tutorials.github.io/ECML-Demo-2025/.
aeon, Classification, Regression, Time series
432-437
Middlehurst, Matthew
44ae267d-b9ec-42b2-b818-d901b221daf9
Bagnall, Anthony
d31e6506-2a00-4358-ba3f-baefd48d59d8
Forestier, Germain
7101361b-4409-4c09-970e-3f757451a4f3
Ismail-Fawaz, Ali
3814ceb3-f8fa-475e-955e-d477a28da875
Guillaume, Antoine
77f622b5-b790-499b-b0c9-c38be167129b
2 October 2026
Middlehurst, Matthew
44ae267d-b9ec-42b2-b818-d901b221daf9
Bagnall, Anthony
d31e6506-2a00-4358-ba3f-baefd48d59d8
Forestier, Germain
7101361b-4409-4c09-970e-3f757451a4f3
Ismail-Fawaz, Ali
3814ceb3-f8fa-475e-955e-d477a28da875
Guillaume, Antoine
77f622b5-b790-499b-b0c9-c38be167129b
Middlehurst, Matthew, Bagnall, Anthony, Forestier, Germain, Ismail-Fawaz, Ali and Guillaume, Antoine
(2026)
Time series machine learning with aeon: classification and regression.
Dutra, Inês, Jorge, Alípio M., Soares, Carlos, Gama, João, Pechenizkiy, Mykola, Cortez, Paulo, Pashami, Sepideh, Pasquali, Arian, Moniz, Nuno and Abreu, Pedro H.
(eds.)
In Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track and Demo Track - European Conference, ECML PKDD 2025, Proceedings.
vol. 16022,
Springer Cham.
.
(doi:10.1007/978-3-032-06129-4_26).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We present the classification and regression modules of aeon, a Python library for all machine learning tasks involving time series. aeon follows the scikit-learn API and is compatible with its utilities such as model selection and pipelines. The toolkit contains a wide range of algorithms, including the state-of-the-art and popular benchmarks for each time series learning task. We demonstrate how to use the aeon toolkit for these tasks and give an example of where these algorithms may be useful. More information and an introductory video of the toolkit modules are available on the demo webpage https://aeon-tutorials.github.io/ECML-Demo-2025/.
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More information
e-pub ahead of print date: 2 October 2026
Published date: 2 October 2026
Venue - Dates:
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2025, , Porto, Portugal, 2025-09-15 - 2025-09-19
Keywords:
aeon, Classification, Regression, Time series
Identifiers
Local EPrints ID: 510632
URI: http://eprints.soton.ac.uk/id/eprint/510632
ISSN: 0302-9743
PURE UUID: 0e331c86-6754-4fd0-873c-ef075b59d164
Catalogue record
Date deposited: 14 Apr 2026 16:50
Last modified: 16 Apr 2026 02:10
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Contributors
Author:
Matthew Middlehurst
Author:
Anthony Bagnall
Author:
Germain Forestier
Author:
Ali Ismail-Fawaz
Author:
Antoine Guillaume
Editor:
Inês Dutra
Editor:
Alípio M. Jorge
Editor:
Carlos Soares
Editor:
João Gama
Editor:
Mykola Pechenizkiy
Editor:
Paulo Cortez
Editor:
Sepideh Pashami
Editor:
Arian Pasquali
Editor:
Nuno Moniz
Editor:
Pedro H. Abreu
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