Does this skill support deep learning for time series?

Yes, it provides access to deep learning architectures like InceptionTime, ResNet, and FCN specifically optimized for time series classification and regression.

Is Aeon compatible with scikit-learn?

Yes, Aeon is designed to be scikit-learn compatible, allowing you to use its estimators and transformers within standard scikit-learn pipelines and workflows.

Can I use Aeon for multivariate data?

Absolutely. Aeon supports both univariate and multivariate time series analysis across its core modules including classification, clustering, and regression.

What is the Aeon skill used for?

The Aeon skill is used for specialized machine learning tasks involving time-indexed data, such as forecasting future trends, detecting anomalies, and classifying temporal sequences.

How do I install the necessary library for this skill?

You can quickly set up the environment by running 'uv pip install aeon' or 'pip install aeon' in your terminal.

Aeon Time Series ML

Name: Aeon Time Series ML
Author: plurigrid

byplurigrid

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Data Science & ML

Provides specialized time series machine learning capabilities including classification, forecasting, and anomaly detection via a scikit-learn compatible interface.

Aeon is a comprehensive Python toolkit designed for advanced machine learning on temporal data, offering state-of-the-art algorithms that go beyond standard ML approaches. This skill enables Claude to implement complex time series workflows such as motif discovery, change-point segmentation, and elastic distance metrics. By leveraging specialized architectures like ROCKET and InceptionTime, it allows developers and data scientists to build high-performance models for financial forecasting, industrial sensor monitoring, and physiological signal analysis within a familiar scikit-learn framework.

Key Features

01Advanced time series classification and regression using ROCKET and deep learning.

02Seamless integration with scikit-learn pipelines and transformations.

038 GitHub stars

04Automated forecasting, anomaly detection, and change-point segmentation.

05Specialized temporal distance metrics including Dynamic Time Warping (DTW) and MSM.

06Motif discovery and similarity search for finding repeated temporal patterns.

Use Cases

01Predicting future values in financial markets or demand forecasting models.

02Categorizing complex sequential patterns in healthcare, audio, or motion datasets.

03Detecting anomalies and structural changes in high-frequency sensor data.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi aeon

For use in Claude.ai and ChatGPT

Download Skill