aeon

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

• Python packages: aeon, numpy, scikit-learn, matplotlib
• Data format: 3D NumPy array (n_instances, n_channels, n_timepoints) or 2D (n_instances, n_timepoints) for univariate
• Optional: numba (required for ROCKET, DTW — install via pip install aeon[all_extras])

pip install aeon
pip install aeon[all_extras]  # includes numba, statsmodels for full algorithm support

Quick Start

import numpy as np
from aeon.classification.convolution_based import RocketClassifier
from aeon.datasets import load_unit_test

# Load a small benchmark dataset
X_train, y_train = load_unit_test(split="train")  # shape: (n, 1, timepoints)
X_test, y_test = load_unit_test(split="test")

print(f"Train: {X_train.shape}, classes: {np.unique(y_train)}")