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

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. See references/forecasting.md for forecasters.

Quick Start:

from aeon.forecasting.arima import ARIMA

forecaster = ARIMA(order=(1, 1, 1))
forecaster.fit(y_train)
y_pred = forecaster.predict(fh=[1, 2, 3, 4, 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_regression

# Classification
X_train, y_train = load_classification("ArrowHead", split="train")

# 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: Aeon expects shape (n_samples, n_channels, n_timepoints)

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: ARIMA, TCNForecaster

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

aeon

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Aeon Time Series Machine Learning

Overview

When to Use This Skill

Installation

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