aeon

Files (12)

• SKILL.md
• anomaly_detection.md · references/
• classification.md · references/
• clustering.md · references/
• datasets_benchmarking.md · references/
• distances.md · references/
• forecasting.md · references/
• networks.md · references/
• regression.md · references/
• segmentation.md · references/
• similarity_search.md · references/
• transformations.md · references/

SKILL.md (first lines)

# 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:

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

For deep learning forecasters/classifiers and other optional estimators:

```bash
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:**
```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 (experimental module in aeon 1.x). See `references/forecasting.md` for forecasters.

**Quick Start:**
```python
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)

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