Time Series Machine Learning
Handles time series classification, forecasting, anomaly detection and similarity search.
Install
npx skills add K-Dense-AI/scientific-agent-skills --skill aeon -g -a claude-code -y- Repository
- K-Dense-AI/scientific-agent-skills55.7k
- Category
- Data and ML
- Updated
- 9d ago
- License
- BSD-3-Clause license
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Aeon Time Series Machine Learning
Overview
Aeon is a scikit-learn compatible Python toolkit for time series machine learning (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: Reviewed against aeon 1.6.0 (Python 3.13). Small synthetic checks cover classification, regression, clustering, forecasts, preprocessing, distances, search, segmentation, matrix profiles, metrics, and local dataset I/O. Remote archive and TensorFlow training snippets are illustrative; they were not executed during this review. Reference catalogs are selected methods, not exhaustive lists. See the 1.6 release notes.
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 3.11-3.14. Pin the reviewed release for reproducibility:
uv pip install "aeon==1.6.0"
Install only the extras required by the chosen estimator. The broad optional set is available as:
uv pip install "aeon[all_extras]==1.6.0"
On zsh, quote the extras: uv pip install "aeon[all_extras]==1.6.0".
