Aeon — time series machine learning
Run time series ML with the aeon toolkit via scikit-learn compatible APIs: classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search, with a "does not trigger" routing table to sibling skills
- What
- Run time series ML with the aeon toolkit via scikit-learn compatible APIs: classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search, with a "does not trigger" routing table to sibling skills
- Cost
- Free
- Needs
- Python 3.11-3.14 with uv (or pip) — the aeon package (uv pip install "aeon>=1.6"); optional: "aeon[dl]" for deep-learning estimators (TensorFlow/Keras), stumpy for matrix profiles; your own time series data
- Install
- Copy the installer prompt below into your Muse — your agent does the rest.
Curated by Skill Harbor — @alterlab-ieu's alterlab-aeon skill, listed here with credit to its creator (part of the AlterLab Academic Skills suite): a complete agent reference for time series machine learning with the aeon Python toolkit. It covers classification (Rocket, MiniRocket, HIVECOTEV2, InceptionTime, DTW K-neighbors), regression, clustering (TimeSeriesKMeans), forecasting (ARIMA and friends under the 1.x module layout), anomaly detection (STOMP matrix profiles), segmentation (ClaSP), similarity search (MASS motifs/discords), feature extraction (ROCKET, Catch22), temporal distance metrics (DTW, ERP, LCSS, MSM), deep-learning estimators via the `aeon[dl]` extra, dataset loading and benchmarking, plus quick-start snippets, common workflows (classification pipeline, feature extraction + traditional ML, anomaly detection with visualization), and best practices (normalize, handle missing values, start with ROCKET before deep learning). It is refreshingly current — verified against aeon 1.6.0 with the 1.x module reorganization documented, plus honest gotchas (predict() semantics changed, StompMotif removed). Honest caveats: needs Python 3.11-3.14 and the aeon package (deep-learning extras pull TensorFlow/Keras); deep estimators want GPUs and larger datasets; your time series data comes from you — the skill is guidance and code patterns, not data. MIT licensed. Skill Harbor never reviews the code, review it yourself before use. Discovered via skills.sh.
Version:
Install
Prerequisites: Python 3.11-3.14 with uv (or pip) — the aeon package (uv pip install "aeon>=1.6"); optional: "aeon[dl]" for deep-learning estimators (TensorFlow/Keras), stumpy for matrix profiles; your own time series data Install "Aeon — time series machine learning" for me. It gives my agent @alterlab-ieu's aeon reference: time series classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search through scikit-learn compatible APIs, with algorithm selection guides, quick-start snippets (RocketClassifier, TimeSeriesKMeans, ARIMA, STOMP, ClaSP, MASS), feature extraction (ROCKET, Catch22), distance metrics (DTW and friends), deep-learning architectures, dataset loading/benchmarking, common workflows, and best practices — verified against aeon 1.6.0 including the 1.x module reorganization. Part of the AlterLab Academic Skills suite. MIT licensed. Repository: https://github.com/alterlab-ieu/alterlab-academic-skills/blob/main/skills/domain-specific/alterlab-aeon/SKILL.md 1. Fetch the SKILL.md file (and any helper files) from the repository path into a temporary folder and summarize what it does in one or two sentences. 2. Safety check: review the SKILL.md and scripts for anything suspicious (unexpected network calls, shell commands, credential harvesting). This repo should contain zero secrets in code. Verify that holds here; STOP on any red flag and tell me. 3. Install it as a skill: copy SKILL.md and its helper files into the agent's skills directory, in a folder named "alterlab-aeon". 4. Verify with no network calls: frontmatter valid, files in place. 5. Report what was installed, where, and what I still need to do myself (e.g. uv pip install "aeon>=1.6" (plus "aeon[dl]" and stumpy if wanted); prepare my time series in shape (n_samples, n_channels, n_timepoints); z-normalize before most algorithms). GitHub is optional: if I have a GitHub account or the gh CLI, you may use it; otherwise public access is fine. Never require it unless it's in the prerequisites above. Rules: don't touch anything outside the temp folder and the install target. If anything looks off, stop and ask me.
Questions
How do I install a build?
Every product page includes a copy-paste install prompt. Paste it into your Muse and it sets the build up for you — no manual configuration.
Where does my money go?
Straight to the seller. Skill Harbor never processes payments: checkout happens on the seller’s own page, usually Stripe.
What does the ✓ next to a creator’s name mean?
It means we confirmed the identity of the person behind the listing. It says nothing about the code itself — always check a build before installing it.