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

bash

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

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:

python

from aeon.transformations.collection.feature_based import Catch22

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

Preprocessing:

python

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:

python

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:

python

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:

python

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:

python

from aeon.benchmarking import get_estimator_results

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

Common Workflows

Classification Pipeline

python

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

python

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

python

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

python

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

python

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

NPX Install

SKILL.md Content

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