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Agent skill

Aeon Time Series Machine Learning

by davila7 in davila7/claude-code-templates

Guides time series machine learning with the aeon toolkit: classification, regression, clustering, forecasting, anomaly detection, segmentation and similarity search.

MITAuto-check passedData & Analytics

Install Aeon Time Series Machine Learning

skills CLI
$ npx skills add davila7/claude-code-templates --skill aeon -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates aeon --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/aeon .claude/skills/aeon && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
aeon
GitHub stars
32k
Used in
14 other repos
Token cost
~2.6k tokens
SKILL.md length
489 words
Files
12 (incl. references)
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Guides time series machine learning with the aeon toolkit: classification, regression, clustering, forecasting, anomaly detection, segmentation and similarity search.

  • Works in 7 steps: Time Series Classification → Time Series Regression → Time Series Clustering → …
  • Classifying or predicting from labeled time series data
  • SKILL.md covers Overview, When to Use This Skill, Installation and Core Capabilities, plus 8 more sections
  • Calls uv

What it does

Aeon is a scikit-learn compatible Python toolkit for time series, and this skill shows the agent how to use it across seven task types: classification, regression, clustering, forecasting, anomaly detection, segmentation and similarity search. Each section gives a quick-start snippet and points to a reference file, such as `references/classification.md` and `references/forecasting.md`, with further notes on distances, transformations, networks and dataset benchmarking.

Algorithm selection guidance for classification pairs goals with choices: MiniRocket and Arsenal for speed, HIVECOTEV2 and InceptionTime for accuracy, shapelet and Catch22 classifiers for interpretability, and nearest-neighbor classifiers with DTW distance for small datasets. Other quick starts use a Rocket regressor, time series k-means, an ARIMA forecaster and the STOMP anomaly detector. Installation is through `uv pip install aeon`, and the excerpt is cut off at the segmentation section.

When your agent uses it

  • Classifying or predicting from labeled time series data
  • Detecting anomalies or change points in temporal sequences
  • Clustering similar series or finding repeated motifs and unusual subsequences
  • Forecasting future values from a time-indexed series
  • Comparing series with specialized distance metrics

Example prompts

  • “Train a ROCKET classifier on the labeled sensor series in data/train.csv and report accuracy.”
  • “Find anomalies in my server latency series with the STOMP detector.”
  • “Cluster these daily electricity load curves with time series k-means.”
  • “Forecast the next month of sales from data/sales.csv using ARIMA.”

Requirements

  • Python with the `aeon` package, installed via `uv pip install aeon`

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Time Series Classification
  2. Time Series Regression
  3. Time Series Clustering
  4. Forecasting
  5. Anomaly Detection
  6. Segmentation
  7. Similarity Search

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • aeon-toolkit.org
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Aeon Time Series Machine Learning loads about 2.6k tokens when it runs, and up to ~18k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 489 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~18k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 14680ec, republished under its MIT licence (© davila7). 489 words, ~2,629 tokens.

Download SKILL.mdSave it as .claude/skills/aeon/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.
name
aeon
description
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. It provides state-of-the-art algorithms for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.

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

bash
uv pip install aeon

Core Capabilities

1. Time Series Classification

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

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

# Train classifier
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
2. Time Series Regression

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)
3. Time Series Clustering

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_
4. Forecasting

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])
5. Anomaly Detection

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)

# Higher scores indicate anomalies
threshold = np.percentile(anomaly_scores, 95)
anomalies = anomaly_scores > threshold
6. Segmentation

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)

Find similar patterns within or across time series. See references/similarity_search.md.

Quick Start:

python
from aeon.similarity_search import StompMotif

# Find recurring patterns
motif_finder = StompMotif(window_size=50, k=3)
motifs = motif_finder.fit_predict(y)

Feature Extraction and Transformations

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)

# Use features with any sklearn classifier
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)

Distance Metrics

Specialized temporal distance measures. See references/distances.md for complete catalog.

Usage:

python
from aeon.distances import dtw_distance, dtw_pairwise_distance

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

# Pairwise distances
distance_matrix = dtw_pairwise_distance(X_train)

# Use with classifiers
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

Deep Learning Networks

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)

Datasets and Benchmarking

Load standard benchmarks and evaluate performance. See references/datasets_benchmarking.md.

Load Datasets:

python
from aeon.datasets import load_classification, load_regression

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

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

Benchmarking:

python
from aeon.benchmarking import get_estimator_results

# Compare with published results
published = get_estimator_results("ROCKET", "GunPoint")

Common Workflows

Classification Pipeline
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
python
from aeon.transformations.collection import RocketTransformer
from sklearn.ensemble import GradientBoostingClassifier

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

# Train traditional ML
clf = GradientBoostingClassifier()
clf.fit(X_train_features, y_train)
predictions = clf.predict(X_test_features)
Show full SKILL.md (204 more words)Show less
Anomaly Detection with Visualization
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()

Best Practices

Data Preparation

  1. 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)

  2. Handle Missing Values: Impute before analysis

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

  3. Check Data Format: Aeon expects shape (n_samples, n_channels, n_timepoints)

Model Selection
  1. Start Simple: Begin with ROCKET variants before deep learning
  2. Use Validation: Split training data for hyperparameter tuning
  3. Compare Baselines: Test against simple methods (1-NN Euclidean, Naive)
  4. Consider Resources: ROCKET for speed, deep learning if GPU available
Algorithm Selection Guide

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)

Reference Documentation

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

Additional Resources

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 11 other files (references) in cli-tool/components/skills/scientific/aeon of davila7/claude-code-templates.

  • SKILL.md
  • references/anomaly_detection.md
  • references/classification.md
  • references/clustering.md
  • references/datasets_benchmarking.md
  • references/distances.md
  • references/forecasting.md
  • references/networks.md
  • references/regression.md
  • references/segmentation.md
  • references/similarity_search.md
  • references/transformations.md

Open the folder on GitHubat commit 14680ec

Used in 14 other repositories

We found 27 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 14 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Aeon Time Series Machine Learning

What does Aeon Time Series Machine Learning do?

Guides time series machine learning with the aeon toolkit: classification, regression, clustering, forecasting, anomaly detection, segmentation and similarity search. Aeon is a scikit-learn compatible Python toolkit for time series, and this skill shows the agent how to use it across seven task types: classification, regression, clustering, forecasting, anomaly detection, segmentation and similarity search.md`, with further notes on distances, transformations, networks and dataset benchmarking.

When should I use Aeon Time Series Machine Learning?

Aeon Time Series Machine Learning fits situations like: classifying or predicting from labeled time series data; detecting anomalies or change points in temporal sequences; clustering similar series or finding repeated motifs and unusual subsequences; forecasting future values from a time-indexed series.

How do I install Aeon Time Series Machine Learning in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill aeon -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/aeon in davila7/claude-code-templates) into .claude/skills/aeon in your project. Claude Code loads it when a task matches its description.

How do I install Aeon Time Series Machine Learning in Codex?

Run `npx skills add davila7/claude-code-templates --skill aeon -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/aeon in davila7/claude-code-templates) into .agents/skills/aeon in your project. Codex loads it when a task matches its description.

Can I use Aeon Time Series Machine Learning in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill aeon -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/aeon, .gemini/skills/aeon, .github/skills/aeon and .opencode/skills/aeon in your project.

What does Aeon Time Series Machine Learning need to run?

Going by SKILL.md and its folder, Aeon Time Series Machine Learning needs the command-line tools its instructions call (uv). Our summary lists: Python with the `aeon` package, installed via `uv pip install aeon`.

Does Aeon Time Series Machine Learning access the network?

SKILL.md names 2 domains. As links in the text: aeon-toolkit.org and github.com. This is read from the text; nothing was executed.

Is Aeon Time Series Machine Learning safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Aeon Time Series Machine Learning use?

Aeon Time Series Machine Learning is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Aeon Time Series Machine Learning use?

About 2.6k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.

What are the alternatives to Aeon Time Series Machine Learning?

Skills that share tags, products or a category with Aeon Time Series Machine Learning: Time Series Analytics User (open-edge-platform/edge-ai-libraries, 169 stars), TimesFM Forecasting (google-research/timesfm, 34k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars) and Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Aeon Time Series Machine Learning?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.