aeon

Aeon Time Series Machine Learning Capabilities

Skill Overview

Aeon is a scikit-learn-compatible Python toolkit for time series machine learning, offering state-of-the-art algorithms for classification, regression, clustering, forecasting, anomaly detection, and more. It is suitable for both univariate and multivariate time series analysis.

Applicable Scenarios

1. Time Series Analysis and Modeling

When you need to perform classification, regression, or clustering on time series data, Aeon provides a complete toolchain from traditional algorithms to deep learning. It is particularly well suited for scenarios that require handling temporal dependencies, such as financial forecasting, IoT sensor analysis, and medical signal processing.

2. Anomaly Detection and Pattern Discovery

When you need to find outliers, change points, or recurring patterns in time series, Aeon’s algorithms like STOMP and ClaSP can efficiently identify anomalies and motifs. This is applicable to industrial equipment fault prediction, network intrusion detection, quality monitoring, and similar scenarios.

3. Rapid Prototyping and Research Validation

Aeon’s scikit-learn-compatible API design allows developers familiar with traditional machine learning to get started quickly, while providing rich benchmark datasets and evaluation tools. It is ideal for academic research, algorithm comparisons, and fast idea validation.

Core Features

1. Seven Major Time Series Tasks

Aeon supports seven major tasks: time series classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Each task offers multiple algorithm choices. From the fast ROCKET family to the high-accuracy HIVE-COTE V2, from interpretable Shapelets to end-to-end deep learning networks, it covers different accuracy and speed needs.

2. Professional Time Series Distance Measures

Built-in elastic distance measures such as DTW, DDTW, WDTW, ERP, EDR, LCSS, TWE, MSM, as well as lock-step measures like Euclidean distance. These time-series-specific distance algorithms better handle time warping, length inconsistencies, and other issues, forming the core foundation for distance-based methods.

3. Feature Extraction and Preprocessing

Provides various feature engineering methods such as ROCKET feature transforms, Catch22 statistical features, and TSFresh features, and supports preprocessing like Z-normalization and missing value imputation. Time series can be converted into feature vectors to work with any scikit-learn-compatible classifier.

Frequently Asked Questions

What is Aeon? What is it mainly used for?

Aeon is a Python library specialized in time series machine learning, offering a complete set of algorithms from classification and regression to forecasting and anomaly detection. Its main feature is scikit-learn API compatibility, allowing developers to handle time series in a familiar way, and it supports both univariate and multivariate time series analysis.

Which algorithm should be chosen for time series classification?

For rapid prototyping, MiniRocketClassifier is recommended for its speed and strong performance; for the highest accuracy, use HIVE-COTE V2 or InceptionTime; for small datasets, KNeighborsTimeSeriesClassifier with DTW distance is suggested; when interpretability is needed, choose ShapeletTransformClassifier or Catch22Classifier.

Which time series algorithms does Aeon support?

Aeon covers the seven main time series tasks: classification (ROCKET, HIVE-COTE, InceptionTime, etc.), regression, clustering (TimeSeriesKMeans), forecasting (ARIMA, etc.), anomaly detection (STOMP), segmentation (ClaSP), and similarity search. It also supports various deep learning architectures such as convolutional networks, recurrent networks, and autoencoders.