JMLR Journal 2024 Journal Article
aeon: a Python Toolkit for Learning from Time Series
- Matthew Middlehurst
- Ali Ismail-Fawaz
- Antoine Guillaume
- Christopher Holder
- David Guijo-Rubio
- Guzal Bulatova
- Leonidas Tsaprounis
- Lukasz Mentel
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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2024. ( edit, beta )