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Aeon Time Series Machine Learning

Aeon时间序列机器学习

Overview

概述

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.

Aeon是一个兼容scikit-learn的Python时间序列机器学习工具包，提供用于分类、回归、聚类、预测、异常检测、分割和相似性搜索的最先进算法。

When to Use This Skill

何时使用该技能

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

在以下场景中应用该技能：

对时间序列数据进行分类或预测
检测时序序列中的异常点或变化点
聚类相似的时间序列模式
预测未来值
查找重复模式（基元）或异常子序列（异常片段）
使用专用距离指标比较时间序列
从时序数据中提取特征

Installation

安装

bash

uv pip install aeon

bash

uv pip install aeon

Core Capabilities

核心功能

1. Time Series Classification

1. 时间序列分类

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

将时间序列分类为预定义类别。完整算法目录请参考

references/classification.md

。

快速入门：

python

from aeon.classification.convolution_based import RocketClassifier
from aeon.datasets import load_classification

Load data

加载数据

X_train, y_train = load_classification("GunPoint", split="train") X_test, y_test = load_classification("GunPoint", split="test")

Train classifier

训练分类器

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

clf = RocketClassifier(n_kernels=10000) clf.fit(X_train, y_train) accuracy = clf.score(X_test, y_test)


**算法选择：**
- **速度与性能兼顾**：`MiniRocketClassifier`、`Arsenal`
- **最高准确率**：`HIVECOTEV2`、`InceptionTimeClassifier`
- **可解释性**：`ShapeletTransformClassifier`、`Catch22Classifier`
- **小数据集**：搭配DTW距离的`KNeighborsTimeSeriesClassifier`

2. Time Series Regression

2. 时间序列回归

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)

从时间序列中预测连续值。算法详情请参考

references/regression.md

。

快速入门：

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

3. 时间序列聚类

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_

在无标签的情况下对相似时间序列进行分组。方法详情请参考

references/clustering.md

。

快速入门：

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

4. 时间序列预测

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])

预测时间序列的未来值。预测器详情请参考

references/forecasting.md

。

快速入门：

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

5. 异常检测

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)

识别异常模式或离群值。检测器详情请参考

references/anomaly_detection.md

。

快速入门：

python

from aeon.anomaly_detection import STOMP

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

Higher scores indicate anomalies

分数越高表示越可能是异常

threshold = np.percentile(anomaly_scores, 95) anomalies = anomaly_scores > threshold

undefined

threshold = np.percentile(anomaly_scores, 95) anomalies = anomaly_scores > threshold

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6. Segmentation

6. 时间序列分割

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)

将时间序列划分为包含变化点的区域。详情请参考

references/segmentation.md

。

快速入门：

python

from aeon.segmentation import ClaSPSegmenter

segmenter = ClaSPSegmenter()
change_points = segmenter.fit_predict(y)

7. Similarity Search

7. 相似性搜索

Find similar patterns within or across time series. See

references/similarity_search.md

Quick Start:

python

from aeon.similarity_search import StompMotif

在单个或多个时间序列中查找相似模式。详情请参考

references/similarity_search.md

。

快速入门：

python

from aeon.similarity_search import StompMotif

Find recurring patterns

查找重复模式

motif_finder = StompMotif(window_size=50, k=3) motifs = motif_finder.fit_predict(y)

undefined

motif_finder = StompMotif(window_size=50, k=3) motifs = motif_finder.fit_predict(y)

undefined

Feature Extraction and Transformations

特征提取与转换

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)

转换时间序列以进行特征工程。详情请参考

references/transformations.md

。

ROCKET特征：

python

from aeon.transformations.collection.convolution_based import RocketTransformer

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

Use features with any sklearn classifier

将特征与任意sklearn分类器配合使用

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)

from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier() clf.fit(X_features, y_train)


**统计特征：**
```python
from aeon.transformations.collection.feature_based import Catch22

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

预处理：

python

from aeon.transformations.collection import MinMaxScaler, Normalizer

scaler = Normalizer()  # Z标准化
X_normalized = scaler.fit_transform(X_train)

Distance Metrics

距离指标

Specialized temporal distance measures. See

references/distances.md

for complete catalog.

Usage:

python

from aeon.distances import dtw_distance, dtw_pairwise_distance

专用的时序距离度量。完整目录请参考

references/distances.md

。

使用方法：

python

from aeon.distances import dtw_distance, dtw_pairwise_distance

Single distance

单个距离计算

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

Pairwise distances

成对距离计算

distance_matrix = dtw_pairwise_distance(X_train)

Use with classifiers

与分类器配合使用

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

from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier

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


**可用距离类型：**
- **弹性距离**：DTW、DDTW、WDTW、ERP、EDR、LCSS、TWE、MSM
- **同步距离**：欧氏距离、曼哈顿距离、闵可夫斯基距离
- **基于形状的距离**：Shape DTW、SBD

Deep Learning Networks

深度学习网络

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)

用于时间序列的神经架构。详情请参考

references/networks.md

。

架构类型：

卷积类：

FCNClassifier

、

ResNetClassifier

、

InceptionTimeClassifier

循环类：
```
RecurrentNetwork
```
、
```
TCNNetwork
```
自编码器类：
```
AEFCNClusterer
```
、
```
AEResNetClusterer
```

使用方法：

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

数据集与基准测试

Load standard benchmarks and evaluate performance. See

references/datasets_benchmarking.md

Load Datasets:

python

from aeon.datasets import load_classification, load_regression

加载标准基准数据集并评估性能。详情请参考

references/datasets_benchmarking.md

。

加载数据集：

python

from aeon.datasets import load_classification, load_regression

Classification

分类数据集

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

Regression

回归数据集

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


**Benchmarking:**
```python
from aeon.benchmarking import get_estimator_results

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


**基准测试：**
```python
from aeon.benchmarking import get_estimator_results

Compare with published results

与已发布结果对比

published = get_estimator_results("ROCKET", "GunPoint")

undefined

published = get_estimator_results("ROCKET", "GunPoint")

undefined

Common Workflows

常见工作流

Classification Pipeline

分类流水线

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)

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

特征提取 + 传统机器学习

python

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

python

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

Extract features

提取特征

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

Train traditional ML

训练传统机器学习模型

clf = GradientBoostingClassifier() clf.fit(X_train_features, y_train) predictions = clf.predict(X_test_features)

undefined

clf = GradientBoostingClassifier() clf.fit(X_train_features, y_train) predictions = clf.predict(X_test_features)

undefined

Anomaly Detection with Visualization

异常检测与可视化

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()

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='时间序列')
plt.subplot(2, 1, 2)
plt.plot(scores, label='异常分数', color='red')
plt.axhline(np.percentile(scores, 95), color='k', linestyle='--')
plt.show()

Best Practices

最佳实践

Data Preparation

数据准备

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)
```

标准化：大多数算法受益于Z标准化

python

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

处理缺失值：分析前进行插补

python

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

检查数据格式：Aeon要求数据形状为
```
(n_samples, n_channels, n_timepoints)
```

Model Selection

模型选择

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

从简单开始：在尝试深度学习之前，先从ROCKET变体开始
使用验证集：拆分训练数据进行超参数调优
对比基线模型：与简单方法（1-NN欧氏距离、朴素模型）进行测试
考虑资源情况：追求速度选ROCKET，若有GPU可用再考虑深度学习

Algorithm Selection Guide

算法选择指南

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)

快速原型开发：

分类：
```
MiniRocketClassifier
```
回归：
```
MiniRocketRegressor
```
聚类：搭配欧氏距离的
```
TimeSeriesKMeans
```

最高准确率：

分类：
```
HIVECOTEV2
```
、
```
InceptionTimeClassifier
```
回归：
```
InceptionTimeRegressor
```
预测：
```
ARIMA
```
、
```
TCNForecaster
```

可解释性优先：

分类：

ShapeletTransformClassifier

、

Catch22Classifier

特征：
```
Catch22
```
、
```
TSFresh
```

小数据集场景：

基于距离的模型：搭配DTW距离的
```
KNeighborsTimeSeriesClassifier
```
避免使用：深度学习（需要大量数据）

Reference Documentation

参考文档

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

详细信息请查看

references/

```
classification.md
```
- 所有分类算法
```
regression.md
```
- 回归方法
```
clustering.md
```
- 聚类算法
```
forecasting.md
```
- 预测方法
```
anomaly_detection.md
```
- 异常检测方法
```
segmentation.md
```
- 分割算法
```
similarity_search.md
```
- 模式匹配与基元发现
```
transformations.md
```
- 特征提取与预处理
```
distances.md
```
- 时间序列距离指标
```
networks.md
```
- 深度学习架构
```
datasets_benchmarking.md
```
- 数据加载与评估工具

Additional Resources

额外资源

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

官方文档：https://www.aeon-toolkit.org/
GitHub仓库：https://github.com/aeon-toolkit/aeon
示例：https://www.aeon-toolkit.org/en/stable/examples.html
API参考：https://www.aeon-toolkit.org/en/stable/api_reference.html