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This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search.
Passed 23 of 24 test cases with the skill, and 15 of 24 without it.
and 10 more in the full report
8 fixed15 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
I need to project future values for a single numerical time series sequence across a 5-step horizon using ARIMA from the aeon package. Could you write a Python code example demonstrating how to instantiate the ARIMA forecaster using its order parameters, fit it on a 1D array of observations, and perform an iterative multi-step forecast to retrieve predicted future values?
| Checked | Without skill | With skill |
|---|---|---|
| Instantiates ARIMA with individual keyword arguments p, d, and q. | Missed | Passed |
| Calls iterative_forecast with prediction_horizon=5. | Missed | Passed |
The instructions your model reads when the skill loads.
Aeon is a scikit-learn compatible Python toolkit for time series machine learning (aeon-toolkit.org). It provides algorithms across classification, regression, clustering, forecasting, anomaly detection, segmentation, similarity search, distances, transformations, benchmarking, and visualization — with a consistent estimator API.
Version note: Reviewed against aeon 1.6.0 (Python 3.13). Small synthetic checks cover classification, regression, clustering, forecasts, preprocessing, distances, search, segmentation, matrix profiles, metrics, and local dataset I/O. Remote archive and TensorFlow training snippets are illustrative; they were not executed during this review. Reference catalogs are selected methods, not exhaustive lists. See the 1.6 release notes.
Apply this skill when:
Requires Python 3.11-3.14. Pin the reviewed release for reproducibility:
uv pip install "aeon==1.6.0"
Install only the extras required by the chosen estimator. The broad optional set is available as:
uv pip install "aeon[all_extras]==1.6.0"
On zsh, quote the extras: uv pip install "aeon[all_extras]==1.6.0".
For the matrix-profile examples: uv pip install "aeon==1.6.0" stumpy. Range precision/recall/F-score depend on prts, whose current NumPy<2 requirement conflicts with aeon 1.6; use the runnable AUC metrics or a separately validated environment. Deep learning estimators use TensorFlow. Inspect an estimator's python_dependencies tag before installing optional packages.
Upstream treats forecasting, anomaly_detection, segmentation, similarity_search, and visualisation as experimental — interfaces may change between minor releases. Prefer stable modules (classification, regression, clustering, distances, transformations) for production pipelines unless you need these tasks.
test cases passed, vs 15 / 24 without
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