Aeon

--- name: aeon description: This skill should be used for time series machine learning tasks, including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use it when working with temporal data, sequential patterns, or time-indexed observations that require specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn-compatible APIs. license: BSD-3-Clause license allowed-tools: Read Write Edit Bash compatibility: Requires Python 3.10+ and the aeon package (uv pip install). Optional aeon[all_extras] for deep learning and extended dependencies. metadata: {"version": "1.0", "skill -author": "K-Dense Inc."} --- # 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 for classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, distances, transformations, benchmarking, and visualization:with a consistent estimator-APIs. 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 Use 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 using 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 = RocketRegressor() reg.fit(X_train, y_train) predictions = reg.predict(X_test) ### 3. Time Series Clustering Group similar time series without labels. See references/ clustering.md for methods. **Quick Start:** python from aeon.clustering import TimeSeriesKMeans clusterer = TimeSeriesKMeans( n_clusters=3, distance="dtw", averaging_method="ba" ) labels = clusterer.fit_predict(X_train) centers = clusterer.cluster_centers_ `` ### 4. Forec