aeon

Overview

aeon provides a unified scikit-learn-compatible API for time series ML tasks: classification, regression, clustering, segmentation, annotation, similarity search, and transformation. It follows the same fit(X, y) / predict(X) pattern as scikit-learn, where X is a 3D NumPy array of shape (n_instances, n_channels, n_timepoints). aeon curates state-of-the-art algorithms from the time series literature — ROCKET and its variants (MiniROCKET, MultiROCKET) for classification, k-means with DTW for clustering, CLASP for segmentation — and provides benchmarking tools for comparing algorithms across datasets. It is the community-maintained fork of sktime following the 2022 governance split.

When to Use

• Classifying ECG, EEG, accelerometer, or sensor time series using state-of-the-art algorithms
• Regressing a scalar target from a time series input (e.g., predicting patient severity from vital sign waveforms)
• Clustering time series by shape similarity when class labels are unavailable
• Detecting change points or segmenting a continuous recording into homogeneous intervals
• Extracting fixed-length feature vectors from variable-length time series for downstream ML
• Benchmarking time series algorithms on the UCR/UEA archive with reproducible comparisons
• Use sktime when you need a larger ecosystem or existing code depends on its API; use tslearn for DTW-focused work

Prerequisites