/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 when working with temporal data, sequential patterns, or time-indexed observations requiring 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: "1.1"
  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 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:

uv pip install "aeon>=1.4,<2"

For deep learning forecasters/classifiers and other optional estimators:

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:**

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:**

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:**

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. Forecasting

Predict future time series values (experimental module in aeon 1.x). See `references/forecasting.md` for forecasters.

**Quick Start:**

import numpy as np
from aeon.forecasting import NaiveForecaster
from aeon.forecasting.stats import ARIMA

y_train = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])

# Set horizon in the constructor; predict passes the series to forecast from
naive = NaiveForecaster(strategy="last", horizon=5)
naive.fit(y_train)
y_pred = naive.predict(y_train)

# ARIMA uses p/d/q (not order=); multi-step via iterative_forecast
arima = ARIMA(p=1, d=1, q=1)
arima.fit(y_train)
y_pred = arima.iterative_forecast(y_train, prediction_horizon=5)

5. Anomaly Detection

Identify unusual patterns or outliers. See `references/anomaly_detection.md` for detectors.

**Quick Start:**

from aeon.anomaly_detection import STOMP

detector = STOMP(window_size=50)
anomaly_scores = detector.fit_predict(y)

# Higher scores indicate anomalies
threshold = np.percentile(anomaly_scores, 95)
anomalies = anomaly_scores > threshold

6. Segmentation

Partition time series into regions with change points. See `references/segmentation.md`.

**Quick Start:**

from aeon.segmentation import ClaSPSegmenter

segmenter = ClaSPSegmenter()
change_points = segmenter.fit_predict(y)

7. Similarity Search

Find similar patterns within or across time series. See `references/similarity_search.md`.

**Quick Start:**

from aeon.similarity_search import StompMotif

# Find recurring patterns
motif_finder = StompMotif(window_size=50, k=3)
motifs = motif_finder.fit_predict(y)

Feature Extraction and Transformations

Transform time series for feature engineering. See `references/transformations.md`.

**ROCKET Features:**