Support for aeon for time-series analysis and machine learning

scientific-packages/aeon/SKILL.md

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+---
+name: aeon
+description: Time series machine learning toolkit for classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use this skill when working with temporal data, performing time series analysis, building predictive models on sequential data, or implementing workflows that involve distance metrics (DTW), transformations (ROCKET, Catch22), or deep learning for time series. Applicable for tasks like ECG classification, stock price forecasting, sensor anomaly detection, or activity recognition from wearable devices.
+---
+
+# Aeon
+
+## Overview
+
+Aeon is a comprehensive Python toolkit for time series machine learning, providing state-of-the-art algorithms and classical techniques for analyzing temporal data. Use this skill when working with sequential/temporal data across seven primary learning tasks: 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 (e.g., ECG classification, activity recognition)
+- Forecasting future values in temporal sequences (e.g., stock prices, energy demand)
+- Detecting anomalies in sensor streams or operational data
+- Clustering temporal patterns or discovering motifs
+- Segmenting time series into meaningful regions (change point detection)
+- Computing distances between time series using specialized metrics (DTW, MSM, ERP)
+- Extracting features from temporal data using ROCKET, Catch22, TSFresh, or shapelets
+- Building deep learning models for time series with specialized architectures
+
+## Core Capabilities
+
+### 1. Time Series Classification
+Classify labeled time series using diverse algorithm families:
+- **Convolution-based**: ROCKET, MiniRocket, MultiRocket, Arsenal, Hydra
+- **Deep learning**: InceptionTime, ResNet, FCN, TimeCNN, LITE
+- **Dictionary-based**: BOSS, TDE, WEASEL, MrSEQL (symbolic representations)
+- **Distance-based**: KNN with elastic distances, Elastic Ensemble, Proximity Forest
+- **Feature-based**: Catch22, FreshPRINCE, Signature classifiers
+- **Interval-based**: CIF, DrCIF, RISE, Random Interval variants
+- **Shapelet-based**: Learning Shapelet, SAST
+- **Hybrid ensembles**: HIVE-COTE V1/V2
+
+Example:
+```python
+from aeon.classification.convolution_based import RocketClassifier
+from aeon.datasets import load_arrow_head
+
+X_train, y_train = load_arrow_head(split="train")
+X_test, y_test = load_arrow_head(split="test")
+
+clf = RocketClassifier()
+clf.fit(X_train, y_train)
+accuracy = clf.score(X_test, y_test)
+```
+
+### 2. Time Series Regression
+Predict continuous values from time series using adapted classification algorithms:
+```python
+from aeon.regression.convolution_based import RocketRegressor
+
+reg = RocketRegressor()
+reg.fit(X_train, y_train_continuous)
+predictions = reg.predict(X_test)
+```
+
+### 3. Forecasting
+Predict future values using statistical and deep learning models:
+- Statistical: ARIMA, ETS, Theta, TAR, AutoTAR, TVP
+- Naive baselines: NaiveForecaster with seasonal strategies
+- Deep learning: TCN (Temporal Convolutional Networks)
+- Regression-based: RegressionForecaster with sliding windows
+
+Example:
+```python
+from aeon.forecasting.naive import NaiveForecaster
+
+forecaster = NaiveForecaster(strategy="last")
+forecaster.fit(y_train)
+y_pred = forecaster.predict(fh=[1, 2, 3])  # forecast 3 steps ahead
+```
+
+### 4. Anomaly Detection
+Identify outliers in time series data:
+- **Distance-based**: KMeansAD, CBLOF, LOF, STOMP, LeftSTAMPi, MERLIN, ROCKAD
+- **Distribution-based**: COPOD, DWT_MLEAD
+- **Outlier detection**: IsolationForest, OneClassSVM, STRAY
+- **Collection adapters**: ClassificationAdapter, OutlierDetectionAdapter
+
+Example:
+```python
+from aeon.anomaly_detection import STOMP
+
+detector = STOMP(window_size=50)
+anomaly_scores = detector.fit_predict(X_series)
+```
+
+### 5. Clustering
+Group similar time series without labels:
+```python
+from aeon.clustering import TimeSeriesKMeans
+
+clusterer = TimeSeriesKMeans(n_clusters=3, distance="dtw")
+clusterer.fit(X_collection)
+labels = clusterer.predict(X_new)
+```
+
+### 6. Segmentation
+Divide time series into distinct regions or identify change points:
+```python
+from aeon.segmentation import ClaSPSegmenter
+
+segmenter = ClaSPSegmenter()
+change_points = segmenter.fit_predict(X_series)
+```
+
+### 7. Similarity Search
+Find motifs and nearest neighbors in time series collections using specialized distance metrics and matrix profile techniques.
+
+### 8. Transformations
+Preprocess and extract features from time series:
+- **Collection transformers**: ROCKET, Catch22, TSFresh, Shapelet, SAX, PAA, SFA
+- **Series transformers**: Moving Average, Box-Cox, PCA, Fourier, Savitzky-Golay
+- **Channel operations**: Selection, scoring, balancing
+- **Data balancing**: SMOTE, ADASYN
+
+Example:
+```python
+from aeon.transformations.collection.convolution_based import Rocket
+
+rocket = Rocket(num_kernels=10000)
+X_transformed = rocket.fit_transform(X_train)
+```
+
+### 9. Distance Metrics
+Compute specialized time series distances:
+- **Warping**: DTW, WDTW, DDTW, WDDTW, Shape DTW, ADTW
+- **Edit distances**: ERP, EDR, LCSS, TWE
+- **Standard**: Euclidean, Manhattan, Minkowski, Squared
+- **Specialized**: MSM, SBD
+
+Example:
+```python
+from aeon.distances import dtw_distance, pairwise_distance
+
+dist = dtw_distance(series1, series2)
+dist_matrix = pairwise_distance(X_collection, metric="dtw")
+```
+
+## Installation
+
+Install aeon using pip:
+```bash
+# Core dependencies only
+pip install -U aeon
+
+# All optional dependencies
+pip install -U "aeon[all_extras]"
+```
+
+Or using conda:
+```bash
+conda create -n aeon-env -c conda-forge aeon
+conda activate aeon-env
+```
+
+**Requirements**: Python 3.9, 3.10, 3.11, or 3.12
+
+## Data Format
+
+Aeon uses standardized data shapes:
+- **Collections**: `(n_cases, n_channels, n_timepoints)` as NumPy arrays or pandas DataFrames
+- **Single series**: NumPy arrays or pandas Series
+- **Variable-length**: Supported with padding or specialized handling
+
+Load example datasets:
+```python
+from aeon.datasets import load_arrow_head, load_airline
+
+# Classification dataset
+X_train, y_train = load_arrow_head(split="train")
+
+# Forecasting dataset
+y = load_airline()
+```
+
+## Workflow Patterns
+
+### Pipeline Construction
+Combine transformers and estimators using scikit-learn pipelines:
+```python
+from sklearn.pipeline import Pipeline
+from aeon.transformations.collection import Catch22
+from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
+
+pipeline = Pipeline([
+    ('features', Catch22()),
+    ('classifier', KNeighborsTimeSeriesClassifier())
+])
+pipeline.fit(X_train, y_train)
+```
+
+### Discovery and Tags
+Find estimators programmatically:
+```python
+from aeon.utils.discovery import all_estimators
+
+# Find all classifiers
+classifiers = all_estimators(type_filter="classifier")
+
+# Find all forecasters
+forecasters = all_estimators(type_filter="forecaster")
+```
+
+## References
+
+The skill includes modular reference files with comprehensive details:
+
+### references/learning_tasks.md
+In-depth coverage of classification, regression, clustering, and similarity search, including algorithm categories, use cases, and code patterns.
+
+### references/temporal_analysis.md
+Detailed information on forecasting, anomaly detection, and segmentation tasks with model descriptions and workflows.
+
+### references/core_modules.md
+Comprehensive documentation of transformations, distances, networks, datasets, and benchmarking utilities.
+
+### references/workflows.md
+Common workflow patterns, pipeline examples, cross-validation strategies, and integration with scikit-learn.
+
+Load these reference files as needed for detailed information on specific modules or workflows.