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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. 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.
aiskillstore/marketplace · ★ 350 · Data & Documents · score 80
Install: claude install-skill aiskillstore/marketplace
# Aeon Time Series Machine Learning ## Overview Aeon is a scikit-learn compatible Python toolkit for time series machine learning. It provides state-of-the-art algorithms for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. ## When to Use This Skill Apply this skill when: - 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 ## Installation ```bash uv pip install aeon ``` ## Core Capabilities ### 1. Time Series Classification Categorize time series into predefined classes. See `references/classification.md` for complete algorithm catalog. **Quick Start:** ```python from aeon.classification.convolution_based import RocketClassifier from aeon.datasets import load_classification # Load data X_train, y_train = load_classification("GunPoint", split="train") X_test, y_test = load_classification("GunPoint", split="test") # Train classifier clf = RocketClassifier(n_kernels=10000) clf.fit(X_train, y_train) accuracy = clf.score(X_test, y_test) ``` **Algorithm Selection:** - **Speed + Performance**: `MiniRocketClassifier`, `Arsenal` - **Maximum Accuracy**: `HIVECOTEV2`, `InceptionTimeClassifier` - **Interpretability**: `Sh