aeon

Aeon Time Series Machine Learning

Overview

Aeon is a scikit-learn compatible Python toolkit for time series machine learning (aeon-toolkit.org). It provides algorithms across classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, distances, transformations, benchmarking, and visualization — with a consistent estimator API.

Version note: Reviewed against aeon 1.6.0 (Python 3.13). Small synthetic checks cover classification, regression, clustering, forecasts, preprocessing, distances, search, segmentation, matrix profiles, metrics, and local dataset I/O. Remote archive and TensorFlow training snippets are illustrative; they were not executed during this review. Reference catalogs are selected methods, not exhaustive lists. See the 1.6 release notes.

When to Use This Skill

Apply this skill when:

• Classifying or predicting from time series data
• Detecting anomalies or change points in temporal sequences
• Clustering similar time series patterns
• Forecasting future values
• Finding repeated patterns (motifs) or unusual subsequences (discords)
• Comparing time series with specialized distance metrics
• Extracting features from temporal data

Installation

Requires Python 3.11-3.14. Pin the reviewed release for reproducibility:

uv pip install "aeon==1.6.0"

Install only the extras required by the chosen estimator. The broad optional set is available as:

uv pip install "aeon[all_extras]==1.6.0"

On zsh, quote the extras: uv pip install "aeon[all_extras]==1.6.0".

For the matrix-profile examples: uv pip install "aeon==1.6.0" stumpy. Range precision/recall/F-score depend on prts, whose current NumPy<2 requirement conflicts with aeon 1.6; use the runnable AUC metrics or a separately validated environment. Deep learning estimators use TensorFlow. Inspect an estimator's python_dependencies tag before installing optional packages.