aeon

combined

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.

When to use

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

Skill page

Conflict

The K-Dense-AI copy is the most detailed and appears to be the source, but it contains a materially different and version-specific API for forecasting (ARIMA p/d/q and iterative_forecast) compared to the other two copies (ARIMA order tuple and predict(fh=...)). The other two copies are near-identical to each other and include sections (Deep Learning Networks, full Segmentation/Similarity Search) that the K-Dense copy truncates or omits. Because the forecasting API conflicts and the copies have different coverage, crowning one would hide a real alternative.