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

1. 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)
   

2. Handle Missing Values: Impute before analysis

   from aeon.transformat