# aeon

> This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

**Use case**: scikit-learn-compatible ML toolkit for time series

**Canonical URL**: https://agentcookbooks.com/skills/aeon/

**Topics**: claude-code, skills, science, data-science

**Trigger phrases**: "time series classification", "temporal anomaly detection", "time series clustering", "univariate time series ML"

**Source**: [K-Dense AI](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/aeon)

**License**: MIT

---

## What it does

`aeon` is a Claude Code skill from K-Dense AI's [scientific-agent-skills repo](https://github.com/K-Dense-AI/scientific-agent-skills). It turns Claude into a time-series ML specialist, covering the full range of temporal learning tasks: classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

The output of a session is working Python code using aeon's scikit-learn-compatible API — fit/predict pipelines, panel datasets, and algorithm-specific parameter choices drawn from the library's extensive collection of time-series-native methods (ROCKET, HIVE-COTE, BOSS, etc.).

Aeon fills the gap that standard scikit-learn leaves open: it treats time as a first-class dimension rather than flattening sequential data into feature vectors.

## When to use it

Reach for it when:

- You have labeled time series and want to train a classifier or regressor that respects temporal structure
- You need anomaly detection or change-point segmentation on sensor streams, physiological signals, or log data
- You want to compare multiple time-series-specific algorithms (ROCKET, WEASEL, DTW-based) under a unified API

When *not* to reach for it:

- Your data is tabular with no meaningful temporal ordering — standard scikit-learn is simpler
- You need deep sequence models (LSTMs, Transformers) — reach for `pytorch-lightning` or `transformers` instead

## Install

Copy the SKILL.md from [scientific-skills/aeon](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/aeon) into `.claude/skills/aeon/`.

The skill activates on trigger phrases including "time series classification", "temporal anomaly detection", and "time series clustering".

## What a session looks like

A typical session has three phases:

1. **Data loading.** Claude shapes your raw time series into aeon's panel format — a 3D array (samples × channels × time points) or a nested DataFrame — and handles unequal-length series if present.
2. **Algorithm selection.** Claude picks an appropriate estimator for the task (e.g., RocketClassifier for accuracy, MiniRocket for speed) and constructs the fit/predict pipeline with sensible defaults.
3. **Evaluation.** Claude generates cross-validation code using aeon's temporal splitters, reports accuracy or F1 per class, and flags any dataset characteristics (class imbalance, variable length) that might affect results.

## Receipts

Honest reporting on what `aeon` handles well and where it falls short:

**Where it works well:**
- Benchmarking multiple classifiers on UCR/UEA archive datasets with minimal boilerplate
- Anomaly detection on multivariate physiological signals where per-channel methods underperform
- Rapid prototyping of time-series pipelines before committing to a custom deep-learning stack

**Where it backfires:**
- Very long time series (hundreds of thousands of time points per sample) can make some algorithms impractically slow; the skill may not proactively flag this
- Forecasting support in aeon is newer and less battle-tested than the classification/clustering modules

**Pattern that works:** start with `RocketClassifier` as a baseline — it is almost always competitive and fits quickly. Then branch to task-specific algorithms only if ROCKET underperforms on your validation set.

## Source and attribution

Originally authored by [K-Dense, Inc.](https://github.com/K-Dense-AI). The canonical SKILL.md lives in the [`aeon` folder](https://github.com/K-Dense-AI/scientific-agent-skills/tree/main/scientific-skills/aeon) of the scientific-agent-skills repository.

License: MIT. Install, adapt, and redistribute with attribution preserved.

This page documents the skill from a practitioner's perspective. For the formal spec and updates, defer to the source repo.