aeon

# Aeon Time Series Machine Learning ## Overview Aeon is a scikit-learn compatible Python toolkit for time series machine learning ([aeon-toolkit.org](https://www.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:** Examples target **aeon 1.x** (stable docs: v1.4.0, March 2026). The v1.0 release reworked forecasting and transformations; import paths differ from aeon 0.x/sktime-era code. ## 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 3.10+** (3.11+ recommended). Pin a 1.x release for reproducibility: ```bash uv pip install "aeon>=1.4,<2" ``` For deep learning forecasters/classifiers and other optional estimators: ```bash uv pip install "aeon[all_extras]>=1.4,<2" ``` On zsh, quote the extras: `uv pip install "aeon[all_extras]>=1.4,<2"`. ### Experimental modules Upstream treats **forecasting**, **anomaly_detection**, **segmentation**, **similarity_search**, and **visualisation** as experimental — interfaces may change between minor releases. Prefer stable modules (classification, regression, clustering, distances, transformations) for production pipelines unless you need these tasks. ## Core Capabilities ### 1. Time Series Classification Categorize time series into predefined classes. See `references/classification.md` for complete algorithm catalog. **Quick Start:** ```python from aeon.classification.convolution_based import RocketClassifier from aeon.datasets import load_classification # Load data X_train, y_train = load_classification("GunPoint", split="train") X_test, y_test = load_classification("GunPoint", split="test") # Train classifier clf = RocketClassifier(n_kernels=10000) clf.fit(X_train, y_train) accuracy = clf.score(X_test, y_test) ``` **Algorithm Selection:** - **Speed + Performance**: `MiniRocketClassifier`, `Arsenal` - **Maximum Accuracy**: `HIVECOTEV2`, `InceptionTimeClassifier` - **Interpretability**: `ShapeletTransformClassifier`, `Catch22Classifier` - **Small Datasets**: `KNeighborsTimeSeriesClassifier` with DTW distance ### 2. Time Series Regression Predict continuous values from time series. See `references/regression.md` for algorithms. **Quick Start:** ```python from aeon.regression.convolution_based import RocketRegressor from aeon.datasets import load_regression X_train, y_train = load_regression("Covid3Month", split="train") X_test, y_test = load_regression("Covid3Month", split="test") reg = RocketRegresso