aeon

Overview of aeon skill

What aeon is for

aeon is a time series machine learning skill for Python workflows that need more than generic tabular ML. It helps with classification, regression, forecasting, clustering, anomaly detection, segmentation, and similarity search on ordered data. If your problem is about timestamps, sequences, or temporal patterns, the aeon skill is a strong fit.

Best-fit users and jobs

Use aeon when you want a scikit-learn-compatible toolkit for univariate or multivariate time series analysis. It is especially useful for analysts and ML engineers who need to turn a raw temporal dataset into a model-ready pipeline without hand-rolling every step. The real job-to-be-done is choosing the right time series method for the task, not just running a generic model.

Why aeon stands out

The main value of aeon is breadth plus compatibility. It covers many time series tasks in one ecosystem, which makes it easier to compare approaches and move from exploration to production-style code. The aeon skill is also practical when you want specialized algorithms or distance measures that standard ML libraries do not provide out of the box.

How to Use aeon skill

Install aeon in your workspace

Install the skill with the repository’s package instructions, then verify your environment matches the Python dependencies you expect to use:

uv pip install aeon

If you are using an agent workflow, the aeon install step should happen before you ask for code generation so the model can rely on the package API rather than guessing it.

Give the skill the right input

The aeon usage pattern works best when you specify four things: task type, data shape, target column or labels, and evaluation goal. For example, “build an aeon forecasting pipeline for daily demand series with missing dates” is much better than “help me analyze time series.” Include whether the input is univariate or multivariate, whether the series lengths are fixed or variable, and whether you need a baseline, a benchmark, or production-ready code.

Start with the right files

Read SKILL.md first, then follow any linked sections for the task you care about most. The repository points to topic-specific references such as classification guidance, so the fastest path is to open the section that matches your use case before asking for implementation details. For aeon, that means looking for the task-specific examples rather than stopping at the overview.

Prompt pattern that works

A useful aeon guide prompt should state the dataset, objective, and constraints in one shot:
“Using aeon, create a scikit-learn-style time series classifier for multivariate sensor data. Assume class imbalance, explain preprocessing needs, and return a minimal train/evaluate example.”
That format reduces guesswork because it tells the skill what kind of pipeline to produce and what tradeoffs matter.

aeon skill FAQ

Is aeon only for forecasting?

No. Forecasting is one use case, but the aeon skill also covers classification, regression, clustering, anomaly detection, segmentation, and similarity search. If your data is temporal but your goal is not prediction of future values, aeon can still be the right choice.

Do I need deep time series expertise to use aeon?

No, but you do need to describe the problem clearly. aeon is suitable for beginners who want a structured time series toolkit, yet better inputs produce much better output. If you can name the task and the data format, the skill can usually guide you to a sensible starting point.

When should I not use aeon?

Do not reach for aeon if your data is not sequential, if a plain tabular model is enough, or if you only need a quick visualization. Also avoid it when your problem is outside time series ML and would be better served by a general-purpose Python or statistics workflow.

How is aeon different from a normal prompt?

A normal prompt may produce generic ML advice. The aeon skill is meant to steer you toward time series-specific choices such as representation, distance metrics, and task-appropriate estimators. That usually means less trial and error, especially for aeon for Data Analysis workflows where the structure of the series matters.

How to Improve aeon skill

Provide series facts, not just goals

The best aeon results come from inputs that describe what the data looks like: number of series, sampling frequency, sequence length, missingness, multivariate channels, and label balance. “Predict churn from monthly usage sequences” is useful; “analyze my data” is not. If you need aeon for Data Analysis, include what you want to compare, explain, or segment.

State the evaluation you care about

Tell the skill how success should be measured. For classification, name the metric and whether false positives or false negatives matter more. For forecasting, specify horizon, backtesting style, and whether intervals are needed. For anomaly detection, say whether you want alerts, ranking, or root-cause candidates.

Watch for common failure modes

The most common issue is under-specifying the time series format, which leads to generic code or the wrong estimator. Another failure mode is asking for a full production system when you only need a reproducible notebook. A stronger aeon guide prompt keeps scope tight and asks for one task at a time.

Iterate with a narrower second prompt

After the first answer, refine with the missing constraint instead of restarting from scratch. For example: “Make this work with variable-length series,” “replace the baseline with a stronger aeon classifier,” or “adapt the example to cross-validation across entities.” This is the fastest way to improve the aeon skill output without introducing extra ambiguity.