aeon

Aeon Time Series Machine Learning

Overview

Aeon is a scikit-learn compatible Python toolkit for time series machine learning (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:

from aeon.transformations.collection.convolution_based import RocketTransformer

rocket = RocketTransformer()
X_features = rocket.fit_transform(X_train)

# Use features with any sklearn classifier
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier()
clf.fit(X_features, y_train)

Statistical Features:

from aeon.transformations.collection.feature_based import Catch22

catch22 = Catch22()
X_features = catch22.fit_transform(X_train)

Preprocessing:

from aeon.transformations.collection import MinMaxScaler, Normalizer

scaler = Normalizer()  # Z-normalization
X_normalized = scaler.fit_transform(X_train)

Distance Metrics

Specialized temporal distance measures. See references/distances.md for complete catalog.

Usage:

from aeon.distances import dtw_distance, dtw_pairwise_distance

# Single distance
distance = dtw_distance(x, y, window=0.1)

# Pairwise distances
distance_matrix = dtw_pairwise_distance(X_train)

# Use with classifiers
from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier

clf = KNeighborsTimeSeriesClassifier(
    n_neighbors=5,
    distance="dtw",
    distance_params={"window": 0.2}
)

Available Distances:

Elastic: DTW, DDTW, WDTW, ERP, EDR, LCSS, TWE, MSM
Lock-step: Euclidean, Manhattan, Minkowski
Shape-based: Shape DTW, SBD

Deep Learning Networks

Neural architectures for time series. See references/networks.md.

Architectures:

Convolutional: FCNClassifier, ResNetClassifier, InceptionTimeClassifier
Recurrent: RecurrentNetwork, TCNNetwork
Autoencoders: AEFCNClusterer, AEResNetClusterer

Usage:

from aeon.classification.deep_learning import InceptionTimeClassifier

clf = InceptionTimeClassifier(n_epochs=100, batch_size=32)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

Datasets and Benchmarking

Load standard benchmarks and evaluate performance. See references/datasets_benchmarking.md.

Load Datasets:

from aeon.datasets import load_classification, load_gunpoint, load_regression

# Classification (generic loader or dataset-specific helper)
X_train, y_train = load_classification("GunPoint", split="train")
X_train, y_train = load_gunpoint(split="train")  # same UCR dataset

# Regression
X_train, y_train = load_regression("Covid3Month", split="train")

Benchmarking:

from aeon.benchmarking import get_estimator_results

# Compare with published results
published = get_estimator_results("ROCKET", "GunPoint")

Common Workflows

Classification Pipeline

from aeon.transformations.collection import Normalizer
from aeon.classification.convolution_based import RocketClassifier
from sklearn.pipeline import Pipeline

pipeline = Pipeline([
    ('normalize', Normalizer()),
    ('classify', RocketClassifier())
])

pipeline.fit(X_train, y_train)
accuracy = pipeline.score(X_test, y_test)

Feature Extraction + Traditional ML

from aeon.transformations.collection import RocketTransformer
from sklearn.ensemble import GradientBoostingClassifier

# Extract features
rocket = RocketTransformer()
X_train_features = rocket.fit_transform(X_train)
X_test_features = rocket.transform(X_test)

# Train traditional ML
clf = GradientBoostingClassifier()
clf.fit(X_train_features, y_train)
predictions = clf.predict(X_test_features)

Anomaly Detection with Visualization

from aeon.anomaly_detection import STOMP
import matplotlib.pyplot as plt

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

plt.figure(figsize=(15, 5))
plt.subplot(2, 1, 1)
plt.plot(y, label='Time Series')
plt.subplot(2, 1, 2)
plt.plot(scores, label='Anomaly Scores', color='red')
plt.axhline(np.percentile(scores, 95), color='k', linestyle='--')
plt.show()

Best Practices

Data Preparation

Normalize: Most algorithms benefit from z-normalization

from aeon.transformations.collection import Normalizer
normalizer = Normalizer()
X_train = normalizer.fit_transform(X_train)
X_test = normalizer.transform(X_test)

Handle Missing Values: Impute before analysis

from aeon.transformations.collection import SimpleImputer
imputer = SimpleImputer(strategy='mean')
X_train = imputer.fit_transform(X_train)

Check Data Format: Collections use (n_cases, n_channels, n_timepoints); single series use (n_channels, n_timepoints) (see data format)

Model Selection

Start Simple: Begin with ROCKET variants before deep learning
Use Validation: Split training data for hyperparameter tuning
Compare Baselines: Test against simple methods (1-NN Euclidean, Naive)
Consider Resources: ROCKET for speed, deep learning if GPU available

Algorithm Selection Guide

For Fast Prototyping:

Classification: MiniRocketClassifier
Regression: MiniRocketRegressor
Clustering: TimeSeriesKMeans with Euclidean

For Maximum Accuracy:

Classification: HIVECOTEV2, InceptionTimeClassifier
Regression: InceptionTimeRegressor
Forecasting: AutoARIMA, AutoETS, TCNForecaster (requires [all_extras] for deep learning)

For Interpretability:

Classification: ShapeletTransformClassifier, Catch22Classifier
Features: Catch22, TSFresh

For Small Datasets:

Distance-based: KNeighborsTimeSeriesClassifier with DTW
Avoid: Deep learning (requires large data)

Reference Documentation

Detailed information available in references/:

classification.md - All classification algorithms
regression.md - Regression methods
clustering.md - Clustering algorithms
forecasting.md - Forecasting approaches
anomaly_detection.md - Anomaly detection methods
segmentation.md - Segmentation algorithms
similarity_search.md - Pattern matching and motif discovery
transformations.md - Feature extraction and preprocessing
distances.md - Time series distance metrics
networks.md - Deep learning architectures
datasets_benchmarking.md - Data loading and evaluation tools

Additional Resources

Documentation: https://www.aeon-toolkit.org/
GitHub: https://github.com/aeon-toolkit/aeon
Examples: https://www.aeon-toolkit.org/en/stable/examples.html
API Reference: https://www.aeon-toolkit.org/en/stable/api_reference.html

Agent operating procedure

Check the environment. Confirm the Python environment and library versions (python -c "import pkg; print(pkg.__version__)") and inspect the data's shape, types and missing values.
Pin down the inputs. Confirm formats, identifiers and parameters from the data or the user. Ask rather than guess any value that changes the result.
Run a small version first. Run on a sample or a single fold first and check runtime and memory.
Execute the full task using the instructions and references above.
Validate the result. Use held-out data, fixed random seeds and appropriate metrics; check for leakage; report uncertainty (CIs, std over seeds).
Report. State what was run (versions, commands, parameters), what was checked, and what is still uncertain.

If this happens	Do this
Out-of-memory or very slow execution	Subsample, use chunked or lazy computation, or reduce model size, and tell the user what changed.
A function, flag or endpoint in these instructions is missing in the installed version	Check the installed version's own documentation (`help()`, `--help`, official docs), adapt, and tell the user. Never invent an API.
A required input, identifier or parameter is ambiguous	Ask the user, or state the assumption explicitly before running.

Integrity rules

Never fabricate results, parameters, identifiers, citations or statistics. If something cannot be run or verified, say so plainly.
Never report a metric you did not compute in this session; show the code path that produced every number.
Treat version-specific details here as possibly outdated: confirm them against the official documentation for the installed version.
Ask before actions that cost money, consume shared GPUs or cloud quota, touch personal or patient data, or cannot be undone.

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About this skill

Overview

When to Use This Skill

Installation

Experimental modules

Core Capabilities

1. Time Series Classification

2. Time Series Regression

3. Time Series Clustering

4. Forecasting

5. Anomaly Detection

6. Segmentation

7. Similarity Search

Feature Extraction and Transformations

Distance Metrics

Deep Learning Networks

Datasets and Benchmarking

Common Workflows

Classification Pipeline

Feature Extraction + Traditional ML

Anomaly Detection with Visualization

Best Practices

Data Preparation

Model Selection

Algorithm Selection Guide

Reference Documentation

Additional Resources

Agent operating procedure

Related skills