aeon: a Python toolkit for learning from time series

Áreas de investigación:

• Sin categoría

Año:

2024

Tipo de publicación:

Artículo

Palabras clave:

Python, Open source, Time series, Machine learning, Data mining, Forecasting, Classification, Extrinsic regression, Clustering

Autores:

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

Journal:

Journal of Machine Learning Research

Volumen:

25

Número:

289

BibTex:

@article{middlehurst2024aeon, 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", 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 this https URL aeon-toolkit/aeon. This version was submitted to the JMLR journal on 02 Nov 2023 for v0.5.0 of aeon. At the time of this preprint aeon has released v0.9.0, and has had substantial changes. ", journal = "Journal of Machine Learning Research", keywords = "Python, Open source, Time series, Machine learning, Data mining, Forecasting, Classification, Extrinsic regression, Clustering", number = "289", title = "{A}eon: a {P}ython toolkit for learning from time series", url = "jmlr.org/papers/v25/23-1444.html", volume = "25", year = "2024", }

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 this https URL aeon-toolkit/aeon. This version was submitted to the JMLR journal on 02 Nov 2023 for v0.5.0 of aeon. At the time of this preprint aeon has released v0.9.0, and has had substantial changes.