aeon
Analyze Time Series with Aeon
General machine-learning methods often miss temporal structure and specialized evaluation needs. This skill guides Aeon model selection, preprocessing, training, and benchmarking for time series tasks.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "aeon" from https://skillstore.io/skills/davila7-aeon.md and its manifest at https://skillstore.io/api/skills/davila7-aeon/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "aeon". Find a fast classifier for 2,000 multichannel sensor samples with 500 timepoints.
Expected outcome:
Start with MiniRocketClassifier after checking Aeon collection shape and applying training-fitted normalization. Compare accuracy and runtime against a one-nearest-neighbor Euclidean baseline.
Using "aeon". Plan anomaly detection evaluation for labeled event windows in one long signal.
Expected outcome:
Use a window-based detector and reserve labeled events for threshold selection. Report range precision, range recall, range F-score, and ROC AUC.
Using "aeon". Compare forecasting methods for monthly demand with a six-month horizon.
Expected outcome:
Use chronological validation and define the forecast horizon before fitting. Compare ARIMA with a naive baseline using absolute and squared error metrics.
Security Audit
SafeAll 99 static findings are false positives caused by ordinary Python imports, machine-learning terms, Markdown formatting, a fixed GPU selector, and documentation links. No prompt injection, secret access, data exfiltration, dynamic code loading, or untrusted command execution was found.
Risk Factors
⚡ Contains scripts (7)
🔑 Env variables (1)
⚙️ External commands (50)
🌐 Network access (4)
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<iframe src="https://skillstore.io/embed/skills/davila7-aeon.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>Academic citations (APA · BibTeX · CFF)
APA citation
davila7. (2026). aeon security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-aeon/audits/9BibTeX citation
@techreport{davila7-davila7-aeon-2026,
author = {davila7},
title = {aeon security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-aeon/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "aeon security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-aeon/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-aeon:audit:9"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-aeon
2026-08-21
k-dense-ai-aeon
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Classify Sensor Signals
Choose a fast baseline, normalize multichannel signals, train an Aeon classifier, and evaluate held-out accuracy.
Forecast Operational Demand
Compare forecasting approaches, define a horizon, validate chronologically, and report errors for planning.
Benchmark Temporal Models
Load standard datasets, compare estimators with task-specific metrics, and apply statistical tests across results.
Try These Prompts
I have [task], [sample count], [channels], and [timepoints]. Recommend one Aeon baseline and explain required preprocessing and evaluation.
Design an Aeon workflow for [task]. Include data shape checks, leakage prevention, train-validation splitting, metrics, and a simple baseline.
Compare [algorithm A], [algorithm B], and [algorithm C] for my [dataset description]. Assess accuracy, speed, interpretability, memory, and tuning needs.
Create an Aeon benchmarking plan for [research question]. Include datasets, estimators, task-specific metrics, cross-validation, statistical tests, resource controls, and reporting criteria.
Best Practices
- Fit preprocessing only on training data, then reuse the fitted transformations on validation and test data.
- Start with a fast baseline before adding ensembles or deep learning models.
- Report data shape, split strategy, random seeds, metrics, runtime, and hardware.
Avoid
- Do not use random splits when they leak future observations into training.
- Do not select complex models without comparing a simple temporal baseline.
- Do not treat anomaly thresholds or forecast horizons as universal across datasets.
Frequently Asked Questions
Does this skill install Aeon?
Which time series tasks are covered?
Can it handle multivariate time series?
Does Aeon work with scikit-learn workflows?
Is a GPU required?
Does the skill send my data anywhere?
Developer Details
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/24/2026
Usage
5 downloads · 526 views
File structure