github.com/aeon-toolkit/aeon @v1.5.0
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README
Time series machine learning, built by the researchers behind the algorithms.
aeon is a scikit-learn compatible Python library for learning from time series.
It covers classification, regression, clustering, forecasting, anomaly detection, distances,
segmentation, similarity search, transformations and benchmarking.
Many implementations in aeon are contributed and maintained by the researchers who developed the original methods. These include state-of-the-art models for forecasting, classification, regression, and clustering, including deep learning approaches.
Documentation · Examples · API reference · Getting started · Discussions · Discord
📄 Published in the Journal of Machine Learning Research (2024) — aeon: a Python Toolkit for Learning from Time Series
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Supported by
From paper to pip install
aeon is developed in close contact with the time series research community.
Many of its algorithms are contributed or maintained by their original authors,
and the same team behind aeon runs the benchmarks that the field uses to
evaluate new methods. That means:
- Faithful implementations. Algorithms reflect what the papers actually describe.
- State of the art, sooner. New methods often land in
aeonalongside publication. - Evidence-based defaults. What's included — and what's recommended — is grounded in published comparative studies.
A selection of algorithms available in aeon written by aeon core developers or contributors:
| Method | Reference | Task |
|---|---|---|
| InceptionTime | Ismail-Fawaz et al., 2020 | Classification |
| Hydra-MultiRocket | Dempster et al., 2023 | Classification |
| HIVE-COTEv2.0 | Middlehurst et al., 2021 | Classification |
| SETAR-Tree | Godahewa et al., 2023 | Forecasting |
| KASBA | Holder et al., 2026 | Clustering |
| CLASP | Ermshaus et al., 2023 | Segmentation |
| DrCIF | Guijo-Rubio et al., 2024 | Regression |
| TDE | Guijo-Rubio et al., 2025 | Ordinal Classification |
Code in aeon and related toolkits has been used in a wide range of benchmarking studies:
| Study | Reference | Area |
|---|---|---|
| Clustering | Holder et al., 2024 | Benchmarking |
| Anomaly detection | Schmidl et al., 2022 | Benchmarking |
| Classification (the "bake off") | Bagnall et al., 2017 | Benchmarking |
| Classification ("bake off redux") | Middlehurst et al., 2025 | Benchmarking |
| Deep learning for classification | Ismail-Fawaz et al., 2019 | Benchmarking |
See the API reference for the full list of estimators across all tasks.
Installation
aeon requires Python 3.10 or newer.
Install the latest release from PyPI:
pip install aeon
To install with all optional dependencies (including deep learning):
pip install aeon[all_extras]
For development installs and platform-specific notes, see the installation guide.
Quick start
Fit a classifier on a standard UCR dataset:
from aeon.classification.convolution_based import RocketClassifier
from aeon.datasets import load_gunpoint
X_train, y_train = load_gunpoint(split="train")
X_test, y_test = load_gunpoint(split="test")
clf = RocketClassifier()
clf.fit(X_train, y_train)
print("Accuracy:", clf.score(X_test, y_test))
Tasks supported
Ten task areas, one consistent API:
| Task | What it does | |
|---|---|---|
| Classification | Predict labels for time series | [docs →](https://www.aeon-toolkit.org/en/sta |