alterlab-aeon

Aeon Time Series Machine Learning

Overview

Aeon is a scikit-learn compatible Python toolkit for time series machine learning. It provides state-of-the-art algorithms for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

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

uv pip install "aeon>=1.4"

Module layout below targets aeon 1.x. Import paths changed across the 0.x→1.x reorg (e.g. forecasters moved under aeon.forecasting.stats, series anomaly detectors under aeon.anomaly_detection.series.*); if an import fails, check the current API reference rather than assuming an older path.

Core Capabilities

1. Time Series Classification

Categorize time series into predefined classes. See references/classification.md for complete algorithm catalog.

Quick Start:

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.