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skills/aeon/references/anomaly_detection.md

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+# Anomaly Detection
+
+Aeon provides anomaly detection methods for identifying unusual patterns in time series at both series and collection levels.
+
+## Collection Anomaly Detectors
+
+Detect anomalous time series within a collection:
+
+- `ClassificationAdapter` - Adapts classifiers for anomaly detection
+  - Train on normal data, flag outliers during prediction
+  - **Use when**: Have labeled normal data, want classification-based approach
+
+- `OutlierDetectionAdapter` - Wraps sklearn outlier detectors
+  - Works with IsolationForest, LOF, OneClassSVM
+  - **Use when**: Want to use sklearn anomaly detectors on collections
+
+## Series Anomaly Detectors
+
+Detect anomalous points or subsequences within a single time series.
+
+### Distance-Based Methods
+
+Use similarity metrics to identify anomalies:
+
+- `CBLOF` - Cluster-Based Local Outlier Factor
+  - Clusters data, identifies outliers based on cluster properties
+  - **Use when**: Anomalies form sparse clusters
+
+- `KMeansAD` - K-means based anomaly detection
+  - Distance to nearest cluster center indicates anomaly
+  - **Use when**: Normal patterns cluster well
+
+- `LeftSTAMPi` - Left STAMP incremental
+  - Matrix profile for online anomaly detection
+  - **Use when**: Streaming data, need online detection
+
+- `STOMP` - Scalable Time series Ordered-search Matrix Profile
+  - Computes matrix profile for subsequence anomalies
+  - **Use when**: Discord discovery, motif detection
+
+- `MERLIN` - Matrix profile-based method
+  - Efficient matrix profile computation
+  - **Use when**: Large time series, need scalability
+
+- `LOF` - Local Outlier Factor adapted for time series
+  - Density-based outlier detection
+  - **Use when**: Anomalies in low-density regions
+
+- `ROCKAD` - ROCKET-based semi-supervised detection
+  - Uses ROCKET features for anomaly identification
+  - **Use when**: Have some labeled data, want feature-based approach
+
+### Distribution-Based Methods
+
+Analyze statistical distributions:
+
+- `COPOD` - Copula-Based Outlier Detection
+  - Models marginal and joint distributions
+  - **Use when**: Multi-dimensional time series, complex dependencies
+
+- `DWT_MLEAD` - Discrete Wavelet Transform Multi-Level Anomaly Detection
+  - Decomposes series into frequency bands
+  - **Use when**: Anomalies at specific frequencies
+
+### Isolation-Based Methods
+
+Use isolation principles:
+
+- `IsolationForest` - Random forest-based isolation
+  - Anomalies easier to isolate than normal points
+  - **Use when**: High-dimensional data, no assumptions about distribution
+
+- `OneClassSVM` - Support vector machine for novelty detection
+  - Learns boundary around normal data
+  - **Use when**: Well-defined normal region, need robust boundary
+
+- `STRAY` - Streaming Robust Anomaly Detection
+  - Robust to data distribution changes
+  - **Use when**: Streaming data, distribution shifts
+
+### External Library Integration
+
+- `PyODAdapter` - Bridges PyOD library to aeon
+  - Access 40+ PyOD anomaly detectors
+  - **Use when**: Need specific PyOD algorithm
+
+## Quick Start
+
+```python
+from aeon.anomaly_detection import STOMP
+import numpy as np
+
+# Create time series with anomaly
+y = np.concatenate([
+    np.sin(np.linspace(0, 10, 100)),
+    [5.0],  # Anomaly spike
+    np.sin(np.linspace(10, 20, 100))
+])
+
+# Detect anomalies
+detector = STOMP(window_size=10)
+anomaly_scores = detector.fit_predict(y)
+
+# Higher scores indicate more anomalous points
+threshold = np.percentile(anomaly_scores, 95)
+anomalies = anomaly_scores > threshold
+```
+
+## Point vs Subsequence Anomalies
+
+- **Point anomalies**: Single unusual values
+  - Use: COPOD, DWT_MLEAD, IsolationForest
+
+- **Subsequence anomalies** (discords): Unusual patterns
+  - Use: STOMP, LeftSTAMPi, MERLIN
+
+- **Collective anomalies**: Groups of points forming unusual pattern
+  - Use: Matrix profile methods, clustering-based
+
+## Evaluation Metrics
+
+Specialized metrics for anomaly detection:
+
+```python
+from aeon.benchmarking.metrics.anomaly_detection import (
+    range_precision,
+    range_recall,
+    range_f_score,
+    roc_auc_score
+)
+
+# Range-based metrics account for window detection
+precision = range_precision(y_true, y_pred, alpha=0.5)
+recall = range_recall(y_true, y_pred, alpha=0.5)
+f1 = range_f_score(y_true, y_pred, alpha=0.5)
+```
+
+## Algorithm Selection
+
+- **Speed priority**: KMeansAD, IsolationForest
+- **Accuracy priority**: STOMP, COPOD
+- **Streaming data**: LeftSTAMPi, STRAY
+- **Discord discovery**: STOMP, MERLIN
+- **Multi-dimensional**: COPOD, PyODAdapter
+- **Semi-supervised**: ROCKAD, OneClassSVM
+- **No training data**: IsolationForest, STOMP
+
+## Best Practices
+
+1. **Normalize data**: Many methods sensitive to scale
+2. **Choose window size**: For matrix profile methods, window size critical
+3. **Set threshold**: Use percentile-based or domain-specific thresholds
+4. **Validate results**: Visualize detections to verify meaningfulness
+5. **Handle seasonality**: Detrend/deseasonalize before detection

skills/aeon/references/classification.md

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+# Time Series Classification
+
+Aeon provides 13 categories of time series classifiers with scikit-learn compatible APIs.
+
+## Convolution-Based Classifiers
+
+Apply random convolutional transformations for efficient feature extraction:
+
+- `Arsenal` - Ensemble of ROCKET classifiers with varied kernels
+- `HydraClassifier` - Multi-resolution convolution with dilation
+- `RocketClassifier` - Random convolution kernels with ridge regression
+- `MiniRocketClassifier` - Simplified ROCKET variant for speed
+- `MultiRocketClassifier` - Combines multiple ROCKET variants
+
+**Use when**: Need fast, scalable classification with strong performance across diverse datasets.
+
+## Deep Learning Classifiers
+
+Neural network architectures optimized for temporal sequences:
+
+- `FCNClassifier` - Fully convolutional network
+- `ResNetClassifier` - Residual networks with skip connections
+- `InceptionTimeClassifier` - Multi-scale inception modules
+- `TimeCNNClassifier` - Standard CNN for time series
+- `MLPClassifier` - Multi-layer perceptron baseline
+- `EncoderClassifier` - Generic encoder wrapper
+- `DisjointCNNClassifier` - Shapelet-focused architecture
+
+**Use when**: Large datasets available, need end-to-end learning, or complex temporal patterns.
+
+## Dictionary-Based Classifiers
+
+Transform time series into symbolic representations:
+
+- `BOSSEnsemble` - Bag-of-SFA-Symbols with ensemble voting
+- `TemporalDictionaryEnsemble` - Multiple dictionary methods combined
+- `WEASEL` - Word ExtrAction for time SEries cLassification
+- `MrSEQLClassifier` - Multiple symbolic sequence learning
+
+**Use when**: Need interpretable models, sparse patterns, or symbolic reasoning.
+
+## Distance-Based Classifiers
+
+Leverage specialized time series distance metrics:
+
+- `KNeighborsTimeSeriesClassifier` - k-NN with temporal distances (DTW, LCSS, ERP, etc.)
+- `ElasticEnsemble` - Combines multiple elastic distance measures
+- `ProximityForest` - Tree ensemble using distance-based splits
+
+**Use when**: Small datasets, need similarity-based classification, or interpretable decisions.
+
+## Feature-Based Classifiers
+
+Extract statistical and signature features before classification:
+
+- `Catch22Classifier` - 22 canonical time-series characteristics
+- `TSFreshClassifier` - Automated feature extraction via tsfresh
+- `SignatureClassifier` - Path signature transformations
+- `SummaryClassifier` - Summary statistics extraction
+- `FreshPRINCEClassifier` - Combines multiple feature extractors
+
+**Use when**: Need interpretable features, domain expertise available, or feature engineering approach.
+
+## Interval-Based Classifiers
+
+Extract features from random or supervised intervals:
+
+- `CanonicalIntervalForestClassifier` - Random interval features with decision trees
+- `DrCIFClassifier` - Diverse Representation CIF with catch22 features
+- `TimeSeriesForestClassifier` - Random intervals with summary statistics
+- `RandomIntervalClassifier` - Simple interval-based approach
+- `RandomIntervalSpectralEnsembleClassifier` - Spectral features from intervals
+- `SupervisedTimeSeriesForest` - Supervised interval selection
+
+**Use when**: Discriminative patterns occur in specific time windows.
+
+## Shapelet-Based Classifiers
+
+Identify discriminative subsequences (shapelets):
+
+- `ShapeletTransformClassifier` - Discovers and uses discriminative shapelets
+- `LearningShapeletClassifier` - Learns shapelets via gradient descent
+- `SASTClassifier` - Scalable approximate shapelet transform
+- `RDSTClassifier` - Random dilated shapelet transform
+
+**Use when**: Need interpretable discriminative patterns or phase-invariant features.
+
+## Hybrid Classifiers
+
+Combine multiple classification paradigms:
+
+- `HIVECOTEV1` - Hierarchical Vote Collective of Transformation-based Ensembles (version 1)
+- `HIVECOTEV2` - Enhanced version with updated components
+
+**Use when**: Maximum accuracy required, computational resources available.
+
+## Early Classification
+
+Make predictions before observing entire time series:
+
+- `TEASER` - Two-tier Early and Accurate Series Classifier
+- `ProbabilityThresholdEarlyClassifier` - Prediction when confidence exceeds threshold
+
+**Use when**: Real-time decisions needed, or observations have cost.
+
+## Ordinal Classification
+
+Handle ordered class labels:
+
+- `OrdinalTDE` - Temporal dictionary ensemble for ordinal outputs
+
+**Use when**: Classes have natural ordering (e.g., severity levels).
+
+## Composition Tools
+
+Build custom pipelines and ensembles:
+
+- `ClassifierPipeline` - Chain transformers with classifiers
+- `WeightedEnsembleClassifier` - Weighted combination of classifiers
+- `SklearnClassifierWrapper` - Adapt sklearn classifiers for time series
+
+## 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 and predict
+clf = RocketClassifier()
+clf.fit(X_train, y_train)
+accuracy = clf.score(X_test, y_test)
+```
+
+## Algorithm Selection
+
+- **Speed priority**: MiniRocketClassifier, Arsenal
+- **Accuracy priority**: HIVECOTEV2, InceptionTimeClassifier
+- **Interpretability**: ShapeletTransformClassifier, Catch22Classifier
+- **Small data**: KNeighborsTimeSeriesClassifier, Distance-based methods
+- **Large data**: Deep learning classifiers, ROCKET variants

skills/aeon/references/clustering.md

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+# Time Series Clustering
+
+Aeon provides clustering algorithms adapted for temporal data with specialized distance metrics and averaging methods.
+
+## Partitioning Algorithms
+
+Standard k-means/k-medoids adapted for time series:
+
+- `TimeSeriesKMeans` - K-means with temporal distance metrics (DTW, Euclidean, etc.)
+- `TimeSeriesKMedoids` - Uses actual time series as cluster centers
+- `TimeSeriesKShape` - Shape-based clustering algorithm
+- `TimeSeriesKernelKMeans` - Kernel-based variant for nonlinear patterns
+
+**Use when**: Known number of clusters, spherical cluster shapes expected.
+
+## Large Dataset Methods
+
+Efficient clustering for large collections:
+
+- `TimeSeriesCLARA` - Clustering Large Applications with sampling
+- `TimeSeriesCLARANS` - Randomized search variant of CLARA
+
+**Use when**: Dataset too large for standard k-medoids, need scalability.
+
+## Elastic Distance Clustering
+
+Specialized for alignment-based similarity:
+
+- `KASBA` - K-means with shift-invariant elastic averaging
+- `ElasticSOM` - Self-organizing map using elastic distances
+
+**Use when**: Time series have temporal shifts or warping.
+
+## Spectral Methods
+
+Graph-based clustering:
+
+- `KSpectralCentroid` - Spectral clustering with centroid computation
+
+**Use when**: Non-convex cluster shapes, need graph-based approach.
+
+## Deep Learning Clustering
+
+Neural network-based clustering with auto-encoders:
+
+- `AEFCNClusterer` - Fully convolutional auto-encoder
+- `AEResNetClusterer` - Residual network auto-encoder
+- `AEDCNNClusterer` - Dilated CNN auto-encoder
+- `AEDRNNClusterer` - Dilated RNN auto-encoder
+- `AEBiGRUClusterer` - Bidirectional GRU auto-encoder
+- `AEAttentionBiGRUClusterer` - Attention-enhanced BiGRU auto-encoder
+
+**Use when**: Large datasets, need learned representations, or complex patterns.
+
+## Feature-Based Clustering
+
+Transform to feature space before clustering:
+
+- `Catch22Clusterer` - Clusters on 22 canonical features
+- `SummaryClusterer` - Uses summary statistics
+- `TSFreshClusterer` - Automated tsfresh features
+
+**Use when**: Raw time series not informative, need interpretable features.
+
+## Composition
+
+Build custom clustering pipelines:
+
+- `ClustererPipeline` - Chain transformers with clusterers
+
+## Averaging Methods
+
+Compute cluster centers for time series:
+
+- `mean_average` - Arithmetic mean
+- `ba_average` - Barycentric averaging with DTW
+- `kasba_average` - Shift-invariant averaging
+- `shift_invariant_average` - General shift-invariant method
+
+**Use when**: Need representative cluster centers for visualization or initialization.
+
+## Quick Start
+
+```python
+from aeon.clustering import TimeSeriesKMeans
+from aeon.datasets import load_classification
+
+# Load data (using classification data for clustering)
+X_train, _ = load_classification("GunPoint", split="train")
+
+# Cluster time series
+clusterer = TimeSeriesKMeans(
+    n_clusters=3,
+    distance="dtw",  # Use DTW distance
+    averaging_method="ba"  # Barycentric averaging
+)
+labels = clusterer.fit_predict(X_train)
+centers = clusterer.cluster_centers_
+```
+
+## Algorithm Selection
+
+- **Speed priority**: TimeSeriesKMeans with Euclidean distance
+- **Temporal alignment**: KASBA, TimeSeriesKMeans with DTW
+- **Large datasets**: TimeSeriesCLARA, TimeSeriesCLARANS
+- **Complex patterns**: Deep learning clusterers
+- **Interpretability**: Catch22Clusterer, SummaryClusterer
+- **Non-convex clusters**: KSpectralCentroid
+
+## Distance Metrics
+
+Compatible distance metrics include:
+- Euclidean, Manhattan, Minkowski (lock-step)
+- DTW, DDTW, WDTW (elastic with alignment)
+- ERP, EDR, LCSS (edit-based)
+- MSM, TWE (specialized elastic)
+
+## Evaluation
+
+Use clustering metrics from sklearn or aeon benchmarking:
+- Silhouette score
+- Davies-Bouldin index
+- Calinski-Harabasz index

skills/aeon/references/datasets_benchmarking.md

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+# Datasets and Benchmarking
+
+Aeon provides comprehensive tools for loading datasets and benchmarking time series algorithms.
+
+## Dataset Loading
+
+### Task-Specific Loaders
+
+**Classification Datasets**:
+```python
+from aeon.datasets import load_classification
+
+# Load train/test split
+X_train, y_train = load_classification("GunPoint", split="train")
+X_test, y_test = load_classification("GunPoint", split="test")
+
+# Load entire dataset
+X, y = load_classification("GunPoint")
+```
+
+**Regression Datasets**:
+```python
+from aeon.datasets import load_regression
+
+X_train, y_train = load_regression("Covid3Month", split="train")
+X_test, y_test = load_regression("Covid3Month", split="test")
+
+# Bulk download
+from aeon.datasets import download_all_regression
+download_all_regression()  # Downloads Monash TSER archive
+```
+
+**Forecasting Datasets**:
+```python
+from aeon.datasets import load_forecasting
+
+# Load from forecastingdata.org
+y, X = load_forecasting("airline", return_X_y=True)
+```
+
+**Anomaly Detection Datasets**:
+```python
+from aeon.datasets import load_anomaly_detection
+
+X, y = load_anomaly_detection("NAB_realKnownCause")
+```
+
+### File Format Loaders
+
+**Load from .ts files**:
+```python
+from aeon.datasets import load_from_ts_file
+
+X, y = load_from_ts_file("path/to/data.ts")
+```
+
+**Load from .tsf files**:
+```python
+from aeon.datasets import load_from_tsf_file
+
+df, metadata = load_from_tsf_file("path/to/data.tsf")
+```
+
+**Load from ARFF files**:
+```python
+from aeon.datasets import load_from_arff_file
+
+X, y = load_from_arff_file("path/to/data.arff")
+```
+
+**Load from TSV files**:
+```python
+from aeon.datasets import load_from_tsv_file
+
+data = load_from_tsv_file("path/to/data.tsv")
+```
+
+**Load TimeEval CSV**:
+```python
+from aeon.datasets import load_from_timeeval_csv_file
+
+X, y = load_from_timeeval_csv_file("path/to/timeeval.csv")
+```
+
+### Writing Datasets
+
+**Write to .ts format**:
+```python
+from aeon.datasets import write_to_ts_file
+
+write_to_ts_file(X, "output.ts", y=y, problem_name="MyDataset")
+```
+
+**Write to ARFF format**:
+```python
+from aeon.datasets import write_to_arff_file
+
+write_to_arff_file(X, "output.arff", y=y)
+```
+
+## Built-in Datasets
+
+Aeon includes several benchmark datasets for quick testing:
+
+### Classification
+- `ArrowHead` - Shape classification
+- `GunPoint` - Gesture recognition
+- `ItalyPowerDemand` - Energy demand
+- `BasicMotions` - Motion classification
+- And 100+ more from UCR/UEA archives
+
+### Regression
+- `Covid3Month` - COVID forecasting
+- Various datasets from Monash TSER archive
+
+### Segmentation
+- Time series segmentation datasets
+- Human activity data
+- Sensor data collections
+
+### Special Collections
+- `RehabPile` - Rehabilitation data (classification & regression)
+
+## Dataset Metadata
+
+Get information about datasets:
+
+```python
+from aeon.datasets import get_dataset_meta_data
+
+metadata = get_dataset_meta_data("GunPoint")
+print(metadata)
+# {'n_train': 50, 'n_test': 150, 'length': 150, 'n_classes': 2, ...}
+```
+
+## Benchmarking Tools
+
+### Loading Published Results
+
+Access pre-computed benchmark results:
+
+```python
+from aeon.benchmarking import get_estimator_results
+
+# Get results for specific algorithm on dataset
+results = get_estimator_results(
+    estimator_name="ROCKET",
+    dataset_name="GunPoint"
+)
+
+# Get all available estimators for a dataset
+estimators = get_available_estimators("GunPoint")
+```
+
+### Resampling Strategies
+
+Create reproducible train/test splits:
+
+```python
+from aeon.benchmarking import stratified_resample
+
+# Stratified resampling maintaining class distribution
+X_train, X_test, y_train, y_test = stratified_resample(
+    X, y,
+    random_state=42,
+    test_size=0.3
+)
+```
+
+### Performance Metrics
+
+Specialized metrics for time series tasks:
+
+**Anomaly Detection Metrics**:
+```python
+from aeon.benchmarking.metrics.anomaly_detection import (
+    range_precision,
+    range_recall,
+    range_f_score,
+    range_roc_auc_score
+)
+
+# Range-based metrics for window detection
+precision = range_precision(y_true, y_pred, alpha=0.5)
+recall = range_recall(y_true, y_pred, alpha=0.5)
+f1 = range_f_score(y_true, y_pred, alpha=0.5)
+auc = range_roc_auc_score(y_true, y_scores)
+```
+
+**Clustering Metrics**:
+```python
+from aeon.benchmarking.metrics.clustering import clustering_accuracy
+
+# Clustering accuracy with label matching
+accuracy = clustering_accuracy(y_true, y_pred)
+```
+
+**Segmentation Metrics**:
+```python
+from aeon.benchmarking.metrics.segmentation import (
+    count_error,
+    hausdorff_error
+)
+
+# Number of change points difference
+count_err = count_error(y_true, y_pred)
+
+# Maximum distance between predicted and true change points
+hausdorff_err = hausdorff_error(y_true, y_pred)
+```
+
+### Statistical Testing
+
+Post-hoc analysis for algorithm comparison:
+
+```python
+from aeon.benchmarking import (
+    nemenyi_test,
+    wilcoxon_test
+)
+
+# Nemenyi test for multiple algorithms
+results = nemenyi_test(scores_matrix, alpha=0.05)
+
+# Pairwise Wilcoxon signed-rank test
+stat, p_value = wilcoxon_test(scores_alg1, scores_alg2)
+```
+
+## Benchmark Collections
+
+### UCR/UEA Time Series Archives
+
+Access to comprehensive benchmark repositories:
+
+```python
+# Classification: 112 univariate + 30 multivariate datasets
+X_train, y_train = load_classification("Chinatown", split="train")
+
+# Automatically downloads from timeseriesclassification.com
+```
+
+### Monash Forecasting Archive
+
+```python
+# Load forecasting datasets
+y = load_forecasting("nn5_daily", return_X_y=False)
+```
+
+### Published Benchmark Results
+
+Pre-computed results from major competitions:
+
+- 2017 Univariate Bake-off
+- 2021 Multivariate Classification
+- 2023 Univariate Bake-off
+
+## Workflow Example
+
+Complete benchmarking workflow:
+
+```python
+from aeon.datasets import load_classification
+from aeon.classification.convolution_based import RocketClassifier
+from aeon.benchmarking import get_estimator_results
+from sklearn.metrics import accuracy_score
+import numpy as np
+
+# Load dataset
+dataset_name = "GunPoint"
+X_train, y_train = load_classification(dataset_name, split="train")
+X_test, y_test = load_classification(dataset_name, split="test")
+
+# Train model
+clf = RocketClassifier(n_kernels=10000, random_state=42)
+clf.fit(X_train, y_train)
+y_pred = clf.predict(X_test)
+
+# Evaluate
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Accuracy: {accuracy:.4f}")
+
+# Compare with published results
+published = get_estimator_results("ROCKET", dataset_name)
+print(f"Published ROCKET accuracy: {published['accuracy']:.4f}")
+```
+
+## Best Practices
+
+### 1. Use Standard Splits
+
+For reproducibility, use provided train/test splits:
+
+```python
+# Good: Use standard splits
+X_train, y_train = load_classification("GunPoint", split="train")
+X_test, y_test = load_classification("GunPoint", split="test")
+
+# Avoid: Creating custom splits
+X, y = load_classification("GunPoint")
+X_train, X_test, y_train, y_test = train_test_split(X, y)
+```
+
+### 2. Set Random Seeds
+
+Ensure reproducibility:
+
+```python
+clf = RocketClassifier(random_state=42)
+results = stratified_resample(X, y, random_state=42)
+```
+
+### 3. Report Multiple Metrics
+
+Don't rely on single metric:
+
+```python
+from sklearn.metrics import accuracy_score, f1_score, precision_score
+
+accuracy = accuracy_score(y_test, y_pred)
+f1 = f1_score(y_test, y_pred, average='weighted')
+precision = precision_score(y_test, y_pred, average='weighted')
+```
+
+### 4. Cross-Validation
+
+For robust evaluation on small datasets:
+
+```python
+from sklearn.model_selection import cross_val_score
+
+scores = cross_val_score(
+    clf, X_train, y_train,
+    cv=5,
+    scoring='accuracy'
+)
+print(f"CV Accuracy: {scores.mean():.4f} (+/- {scores.std():.4f})")
+```
+
+### 5. Compare Against Baselines
+
+Always compare with simple baselines:
+
+```python
+from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
+
+# Simple baseline: 1-NN with Euclidean distance
+baseline = KNeighborsTimeSeriesClassifier(n_neighbors=1, distance="euclidean")
+baseline.fit(X_train, y_train)
+baseline_acc = baseline.score(X_test, y_test)
+
+print(f"Baseline: {baseline_acc:.4f}")
+print(f"Your model: {accuracy:.4f}")
+```
+
+### 6. Statistical Significance
+
+Test if improvements are statistically significant:
+
+```python
+from aeon.benchmarking import wilcoxon_test
+
+# Run on multiple datasets
+accuracies_alg1 = [0.85, 0.92, 0.78, 0.88]
+accuracies_alg2 = [0.83, 0.90, 0.76, 0.86]
+
+stat, p_value = wilcoxon_test(accuracies_alg1, accuracies_alg2)
+if p_value < 0.05:
+    print("Difference is statistically significant")
+```
+
+## Dataset Discovery
+
+Find datasets matching criteria:
+
+```python
+# List all available classification datasets
+from aeon.datasets import get_available_datasets
+
+datasets = get_available_datasets("classification")
+print(f"Found {len(datasets)} classification datasets")
+
+# Filter by properties
+univariate_datasets = [
+    d for d in datasets
+    if get_dataset_meta_data(d)['n_channels'] == 1
+]
+```

skills/aeon/references/distances.md

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+# Distance Metrics
+
+Aeon provides specialized distance functions for measuring similarity between time series, compatible with both aeon and scikit-learn estimators.
+
+## Distance Categories
+
+### Elastic Distances
+
+Allow flexible temporal alignment between series:
+
+**Dynamic Time Warping Family:**
+- `dtw` - Classic Dynamic Time Warping
+- `ddtw` - Derivative DTW (compares derivatives)
+- `wdtw` - Weighted DTW (penalizes warping by location)
+- `wddtw` - Weighted Derivative DTW
+- `shape_dtw` - Shape-based DTW
+
+**Edit-Based:**
+- `erp` - Edit distance with Real Penalty
+- `edr` - Edit Distance on Real sequences
+- `lcss` - Longest Common SubSequence
+- `twe` - Time Warp Edit distance
+
+**Specialized:**
+- `msm` - Move-Split-Merge distance
+- `adtw` - Amerced DTW
+- `sbd` - Shape-Based Distance
+
+**Use when**: Time series may have temporal shifts, speed variations, or phase differences.
+
+### Lock-Step Distances
+
+Compare time series point-by-point without alignment:
+
+- `euclidean` - Euclidean distance (L2 norm)
+- `manhattan` - Manhattan distance (L1 norm)
+- `minkowski` - Generalized Minkowski distance (Lp norm)
+- `squared` - Squared Euclidean distance
+
+**Use when**: Series already aligned, need computational speed, or no temporal warping expected.
+
+## Usage Patterns
+
+### Computing Single Distance
+
+```python
+from aeon.distances import dtw_distance
+
+# Distance between two time series
+distance = dtw_distance(x, y)
+
+# With window constraint (Sakoe-Chiba band)
+distance = dtw_distance(x, y, window=0.1)
+```
+
+### Pairwise Distance Matrix
+
+```python
+from aeon.distances import dtw_pairwise_distance
+
+# All pairwise distances in collection
+X = [series1, series2, series3, series4]
+distance_matrix = dtw_pairwise_distance(X)
+
+# Cross-collection distances
+distance_matrix = dtw_pairwise_distance(X_train, X_test)
+```
+
+### Cost Matrix and Alignment Path
+
+```python
+from aeon.distances import dtw_cost_matrix, dtw_alignment_path
+
+# Get full cost matrix
+cost_matrix = dtw_cost_matrix(x, y)
+
+# Get optimal alignment path
+path = dtw_alignment_path(x, y)
+# Returns indices: [(0,0), (1,1), (2,1), (2,2), ...]
+```
+
+### Using with Estimators
+
+```python
+from aeon.classification.distance_based import KNeighborsTimeSeriesClassifier
+
+# Use DTW distance in classifier
+clf = KNeighborsTimeSeriesClassifier(
+    n_neighbors=5,
+    distance="dtw",
+    distance_params={"window": 0.2}
+)
+clf.fit(X_train, y_train)
+```
+
+## Distance Parameters
+
+### Window Constraints
+
+Limit warping path deviation (improves speed and prevents pathological warping):
+
+```python
+# Sakoe-Chiba band: window as fraction of series length
+dtw_distance(x, y, window=0.1)  # Allow 10% deviation
+
+# Itakura parallelogram: slopes constrain path
+dtw_distance(x, y, itakura_max_slope=2.0)
+```
+
+### Normalization
+
+Control whether to z-normalize series before distance computation:
+
+```python
+# Most elastic distances support normalization
+distance = dtw_distance(x, y, normalize=True)
+```
+
+### Distance-Specific Parameters
+
+```python
+# ERP: penalty for gaps
+distance = erp_distance(x, y, g=0.5)
+
+# TWE: stiffness and penalty parameters
+distance = twe_distance(x, y, nu=0.001, lmbda=1.0)
+
+# LCSS: epsilon threshold for matching
+distance = lcss_distance(x, y, epsilon=0.5)
+```
+
+## Algorithm Selection
+
+### By Use Case:
+
+**Temporal misalignment**: DTW, DDTW, WDTW
+**Speed variations**: DTW with window constraint
+**Shape similarity**: Shape DTW, SBD
+**Edit operations**: ERP, EDR, LCSS
+**Derivative matching**: DDTW
+**Computational speed**: Euclidean, Manhattan
+**Outlier robustness**: Manhattan, LCSS
+
+### By Computational Cost:
+
+**Fastest**: Euclidean (O(n))
+**Fast**: Constrained DTW (O(nw) where w is window)
+**Medium**: Full DTW (O(n²))
+**Slower**: Complex elastic distances (ERP, TWE, MSM)
+
+## Quick Reference Table
+
+| Distance | Alignment | Speed | Robustness | Interpretability |
+|----------|-----------|-------|------------|------------------|
+| Euclidean | Lock-step | Very Fast | Low | High |
+| DTW | Elastic | Medium | Medium | Medium |
+| DDTW | Elastic | Medium | High | Medium |
+| WDTW | Elastic | Medium | Medium | Medium |
+| ERP | Edit-based | Slow | High | Low |
+| LCSS | Edit-based | Slow | Very High | Low |
+| Shape DTW | Elastic | Medium | Medium | High |
+
+## Best Practices
+
+### 1. Normalization
+
+Most distances sensitive to scale; normalize when appropriate:
+
+```python
+from aeon.transformations.collection import Normalizer
+
+normalizer = Normalizer()
+X_normalized = normalizer.fit_transform(X)
+```
+
+### 2. Window Constraints
+
+For DTW variants, use window constraints for speed and better generalization:
+
+```python
+# Start with 10-20% window
+distance = dtw_distance(x, y, window=0.1)
+```
+
+### 3. Series Length
+
+- Equal-length required: Most lock-step distances
+- Unequal-length supported: Elastic distances (DTW, ERP, etc.)
+
+### 4. Multivariate Series
+
+Most distances support multivariate time series:
+
+```python
+# x.shape = (n_channels, n_timepoints)
+distance = dtw_distance(x_multivariate, y_multivariate)
+```
+
+### 5. Performance Optimization
+
+- Use numba-compiled implementations (default in aeon)
+- Consider lock-step distances if alignment not needed
+- Use windowed DTW instead of full DTW
+- Precompute distance matrices for repeated use
+
+### 6. Choosing the Right Distance
+
+```python
+# Quick decision tree:
+if series_aligned:
+    use_distance = "euclidean"
+elif need_speed:
+    use_distance = "dtw"  # with window constraint
+elif temporal_shifts_expected:
+    use_distance = "dtw" or "shape_dtw"
+elif outliers_present:
+    use_distance = "lcss" or "manhattan"
+elif derivatives_matter:
+    use_distance = "ddtw" or "wddtw"
+```
+
+## Integration with scikit-learn
+
+Aeon distances work with sklearn estimators:
+
+```python
+from sklearn.neighbors import KNeighborsClassifier
+from aeon.distances import dtw_pairwise_distance
+
+# Precompute distance matrix
+X_train_distances = dtw_pairwise_distance(X_train)
+
+# Use with sklearn
+clf = KNeighborsClassifier(metric='precomputed')
+clf.fit(X_train_distances, y_train)
+```
+
+## Available Distance Functions
+
+Get list of all available distances:
+
+```python
+from aeon.distances import get_distance_function_names
+
+print(get_distance_function_names())
+# ['dtw', 'ddtw', 'wdtw', 'euclidean', 'erp', 'edr', ...]
+```
+
+Retrieve specific distance function:
+
+```python
+from aeon.distances import get_distance_function
+
+distance_func = get_distance_function("dtw")
+result = distance_func(x, y, window=0.1)
+```

skills/aeon/references/forecasting.md

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+# Time Series Forecasting
+
+Aeon provides forecasting algorithms for predicting future time series values.
+
+## Naive and Baseline Methods
+
+Simple forecasting strategies for comparison:
+
+- `NaiveForecaster` - Multiple strategies: last value, mean, seasonal naive
+  - Parameters: `strategy` ("last", "mean", "seasonal"), `sp` (seasonal period)
+  - **Use when**: Establishing baselines or simple patterns
+
+## Statistical Models
+
+Classical time series forecasting methods:
+
+### ARIMA
+- `ARIMA` - AutoRegressive Integrated Moving Average
+  - Parameters: `p` (AR order), `d` (differencing), `q` (MA order)
+  - **Use when**: Linear patterns, stationary or difference-stationary series
+
+### Exponential Smoothing
+- `ETS` - Error-Trend-Seasonal decomposition
+  - Parameters: `error`, `trend`, `seasonal` types
+  - **Use when**: Trend and seasonal patterns present
+
+### Threshold Autoregressive
+- `TAR` - Threshold Autoregressive model for regime switching
+- `AutoTAR` - Automated threshold discovery
+  - **Use when**: Series exhibits different behaviors in different regimes
+
+### Theta Method
+- `Theta` - Classical Theta forecasting
+  - Parameters: `theta`, `weights` for decomposition
+  - **Use when**: Simple but effective baseline needed
+
+### Time-Varying Parameter
+- `TVP` - Time-varying parameter model with Kalman filtering
+  - **Use when**: Parameters change over time
+
+## Deep Learning Forecasters
+
+Neural networks for complex temporal patterns:
+
+- `TCNForecaster` - Temporal Convolutional Network
+  - Dilated convolutions for large receptive fields
+  - **Use when**: Long sequences, need non-recurrent architecture
+
+- `DeepARNetwork` - Probabilistic forecasting with RNNs
+  - Provides prediction intervals
+  - **Use when**: Need probabilistic forecasts, uncertainty quantification
+
+## Regression-Based Forecasting
+
+Apply regression to lagged features:
+
+- `RegressionForecaster` - Wraps regressors for forecasting
+  - Parameters: `window_length`, `horizon`
+  - **Use when**: Want to use any regressor as forecaster
+
+## Quick Start
+
+```python
+from aeon.forecasting.naive import NaiveForecaster
+from aeon.forecasting.arima import ARIMA
+import numpy as np
+
+# Create time series
+y = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+
+# Naive baseline
+naive = NaiveForecaster(strategy="last")
+naive.fit(y)
+forecast_naive = naive.predict(fh=[1, 2, 3])
+
+# ARIMA model
+arima = ARIMA(order=(1, 1, 1))
+arima.fit(y)
+forecast_arima = arima.predict(fh=[1, 2, 3])
+```
+
+## Forecasting Horizon
+
+The forecasting horizon (`fh`) specifies which future time points to predict:
+
+```python
+# Relative horizon (next 3 steps)
+fh = [1, 2, 3]
+
+# Absolute horizon (specific time indices)
+from aeon.forecasting.base import ForecastingHorizon
+fh = ForecastingHorizon([11, 12, 13], is_relative=False)
+```
+
+## Model Selection
+
+- **Baseline**: NaiveForecaster with seasonal strategy
+- **Linear patterns**: ARIMA
+- **Trend + seasonality**: ETS
+- **Regime changes**: TAR, AutoTAR
+- **Complex patterns**: TCNForecaster
+- **Probabilistic**: DeepARNetwork
+- **Long sequences**: TCNForecaster
+- **Short sequences**: ARIMA, ETS
+
+## Evaluation Metrics
+
+Use standard forecasting metrics:
+
+```python
+from aeon.performance_metrics.forecasting import (
+    mean_absolute_error,
+    mean_squared_error,
+    mean_absolute_percentage_error
+)
+
+# Calculate error
+mae = mean_absolute_error(y_true, y_pred)
+mse = mean_squared_error(y_true, y_pred)
+mape = mean_absolute_percentage_error(y_true, y_pred)
+```
+
+## Exogenous Variables
+
+Many forecasters support exogenous features:
+
+```python
+# Train with exogenous variables
+forecaster.fit(y, X=X_train)
+
+# Predict requires future exogenous values
+y_pred = forecaster.predict(fh=[1, 2, 3], X=X_test)
+```
+
+## Base Classes
+
+- `BaseForecaster` - Abstract base for all forecasters
+- `BaseDeepForecaster` - Base for deep learning forecasters
+
+Extend these to implement custom forecasting algorithms.

skills/aeon/references/networks.md

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+# Deep Learning Networks
+
+Aeon provides neural network architectures specifically designed for time series tasks. These networks serve as building blocks for classification, regression, clustering, and forecasting.
+
+## Core Network Architectures
+
+### Convolutional Networks
+
+**FCNNetwork** - Fully Convolutional Network
+- Three convolutional blocks with batch normalization
+- Global average pooling for dimensionality reduction
+- **Use when**: Need simple yet effective CNN baseline
+
+**ResNetNetwork** - Residual Network
+- Residual blocks with skip connections
+- Prevents vanishing gradients in deep networks
+- **Use when**: Deep networks needed, training stability important
+
+**InceptionNetwork** - Inception Modules
+- Multi-scale feature extraction with parallel convolutions
+- Different kernel sizes capture patterns at various scales
+- **Use when**: Patterns exist at multiple temporal scales
+
+**TimeCNNNetwork** - Standard CNN
+- Basic convolutional architecture
+- **Use when**: Simple CNN sufficient, interpretability valued
+
+**DisjointCNNNetwork** - Separate Pathways
+- Disjoint convolutional pathways
+- **Use when**: Different feature extraction strategies needed
+
+**DCNNNetwork** - Dilated CNN
+- Dilated convolutions for large receptive fields
+- **Use when**: Long-range dependencies without many layers
+
+### Recurrent Networks
+
+**RecurrentNetwork** - RNN/LSTM/GRU
+- Configurable cell type (RNN, LSTM, GRU)
+- Sequential modeling of temporal dependencies
+- **Use when**: Sequential dependencies critical, variable-length series
+
+### Temporal Convolutional Network
+
+**TCNNetwork** - Temporal Convolutional Network
+- Dilated causal convolutions
+- Large receptive field without recurrence
+- **Use when**: Long sequences, need parallelizable architecture
+
+### Multi-Layer Perceptron
+
+**MLPNetwork** - Basic Feedforward
+- Simple fully-connected layers
+- Flattens time series before processing
+- **Use when**: Baseline needed, computational limits, or simple patterns
+
+## Encoder-Based Architectures
+
+Networks designed for representation learning and clustering.
+
+### Autoencoder Variants
+
+**EncoderNetwork** - Generic Encoder
+- Flexible encoder structure
+- **Use when**: Custom encoding needed
+
+**AEFCNNetwork** - FCN-based Autoencoder
+- Fully convolutional encoder-decoder
+- **Use when**: Need convolutional representation learning
+
+**AEResNetNetwork** - ResNet Autoencoder
+- Residual blocks in encoder-decoder
+- **Use when**: Deep autoencoding with skip connections
+
+**AEDCNNNetwork** - Dilated CNN Autoencoder
+- Dilated convolutions for compression
+- **Use when**: Need large receptive field in autoencoder
+
+**AEDRNNNetwork** - Dilated RNN Autoencoder
+- Dilated recurrent connections
+- **Use when**: Sequential patterns with long-range dependencies
+
+**AEBiGRUNetwork** - Bidirectional GRU
+- Bidirectional recurrent encoding
+- **Use when**: Context from both directions helpful
+
+**AEAttentionBiGRUNetwork** - Attention + BiGRU
+- Attention mechanism on BiGRU outputs
+- **Use when**: Need to focus on important time steps
+
+## Specialized Architectures
+
+**LITENetwork** - Lightweight Inception Time Ensemble
+- Efficient inception-based architecture
+- LITEMV variant for multivariate series
+- **Use when**: Need efficiency with strong performance
+
+**DeepARNetwork** - Probabilistic Forecasting
+- Autoregressive RNN for forecasting
+- Produces probabilistic predictions
+- **Use when**: Need forecast uncertainty quantification
+
+## Usage with Estimators
+
+Networks are typically used within estimators, not directly:
+
+```python
+from aeon.classification.deep_learning import FCNClassifier
+from aeon.regression.deep_learning import ResNetRegressor
+from aeon.clustering.deep_learning import AEFCNClusterer
+
+# Classification with FCN
+clf = FCNClassifier(n_epochs=100, batch_size=16)
+clf.fit(X_train, y_train)
+
+# Regression with ResNet
+reg = ResNetRegressor(n_epochs=100)
+reg.fit(X_train, y_train)
+
+# Clustering with autoencoder
+clusterer = AEFCNClusterer(n_clusters=3, n_epochs=100)
+labels = clusterer.fit_predict(X_train)
+```
+
+## Custom Network Configuration
+
+Many networks accept configuration parameters:
+
+```python
+# Configure FCN layers
+clf = FCNClassifier(
+    n_epochs=200,
+    batch_size=32,
+    kernel_size=[7, 5, 3],  # Kernel sizes for each layer
+    n_filters=[128, 256, 128],  # Filters per layer
+    learning_rate=0.001
+)
+```
+
+## Base Classes
+
+- `BaseDeepLearningNetwork` - Abstract base for all networks
+- `BaseDeepRegressor` - Base for deep regression
+- `BaseDeepClassifier` - Base for deep classification
+- `BaseDeepForecaster` - Base for deep forecasting
+
+Extend these to implement custom architectures.
+
+## Training Considerations
+
+### Hyperparameters
+
+Key hyperparameters to tune:
+
+- `n_epochs` - Training iterations (50-200 typical)
+- `batch_size` - Samples per batch (16-64 typical)
+- `learning_rate` - Step size (0.0001-0.01)
+- Network-specific: layers, filters, kernel sizes
+
+### Callbacks
+
+Many networks support callbacks for training monitoring:
+
+```python
+from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
+
+clf = FCNClassifier(
+    n_epochs=200,
+    callbacks=[
+        EarlyStopping(patience=20, restore_best_weights=True),
+        ReduceLROnPlateau(patience=10, factor=0.5)
+    ]
+)
+```
+
+### GPU Acceleration
+
+Deep learning networks benefit from GPU:
+
+```python
+import os
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'  # Use first GPU
+
+# Networks automatically use GPU if available
+clf = InceptionTimeClassifier(n_epochs=100)
+clf.fit(X_train, y_train)
+```
+
+## Architecture Selection
+
+### By Task:
+
+**Classification**: InceptionNetwork, ResNetNetwork, FCNNetwork
+**Regression**: InceptionNetwork, ResNetNetwork, TCNNetwork
+**Forecasting**: TCNNetwork, DeepARNetwork, RecurrentNetwork
+**Clustering**: AEFCNNetwork, AEResNetNetwork, AEAttentionBiGRUNetwork
+
+### By Data Characteristics:
+
+**Long sequences**: TCNNetwork, DCNNNetwork (dilated convolutions)
+**Short sequences**: MLPNetwork, FCNNetwork
+**Multivariate**: InceptionNetwork, FCNNetwork, LITENetwork
+**Variable length**: RecurrentNetwork with masking
+**Multi-scale patterns**: InceptionNetwork
+
+### By Computational Resources:
+
+**Limited compute**: MLPNetwork, LITENetwork
+**Moderate compute**: FCNNetwork, TimeCNNNetwork
+**High compute available**: InceptionNetwork, ResNetNetwork
+**GPU available**: Any deep network (major speedup)
+
+## Best Practices
+
+### 1. Data Preparation
+
+Normalize input data:
+
+```python
+from aeon.transformations.collection import Normalizer
+
+normalizer = Normalizer()
+X_train_norm = normalizer.fit_transform(X_train)
+X_test_norm = normalizer.transform(X_test)
+```
+
+### 2. Training/Validation Split
+
+Use validation set for early stopping:
+
+```python
+from sklearn.model_selection import train_test_split
+
+X_train_fit, X_val, y_train_fit, y_val = train_test_split(
+    X_train, y_train, test_size=0.2, stratify=y_train
+)
+
+clf = FCNClassifier(n_epochs=200)
+clf.fit(X_train_fit, y_train_fit, validation_data=(X_val, y_val))
+```
+
+### 3. Start Simple
+
+Begin with simpler architectures before complex ones:
+
+1. Try MLPNetwork or FCNNetwork first
+2. If insufficient, try ResNetNetwork or InceptionNetwork
+3. Consider ensembles if single models insufficient
+
+### 4. Hyperparameter Tuning
+
+Use grid search or random search:
+
+```python
+from sklearn.model_selection import GridSearchCV
+
+param_grid = {
+    'n_epochs': [100, 200],
+    'batch_size': [16, 32],
+    'learning_rate': [0.001, 0.0001]
+}
+
+clf = FCNClassifier()
+grid = GridSearchCV(clf, param_grid, cv=3)
+grid.fit(X_train, y_train)
+```
+
+### 5. Regularization
+
+Prevent overfitting:
+- Use dropout (if network supports)
+- Early stopping
+- Data augmentation (if available)
+- Reduce model complexity
+
+### 6. Reproducibility
+
+Set random seeds:
+
+```python
+import numpy as np
+import random
+import tensorflow as tf
+
+seed = 42
+np.random.seed(seed)
+random.seed(seed)
+tf.random.set_seed(seed)
+```

skills/aeon/references/regression.md

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+# Time Series Regression
+
+Aeon provides time series regressors across 9 categories for predicting continuous values from temporal sequences.
+
+## Convolution-Based Regressors
+
+Apply convolutional kernels for feature extraction:
+
+- `HydraRegressor` - Multi-resolution dilated convolutions
+- `RocketRegressor` - Random convolutional kernels
+- `MiniRocketRegressor` - Simplified ROCKET for speed
+- `MultiRocketRegressor` - Combined ROCKET variants
+- `MultiRocketHydraRegressor` - Merges ROCKET and Hydra approaches
+
+**Use when**: Need fast regression with strong baseline performance.
+
+## Deep Learning Regressors
+
+Neural architectures for end-to-end temporal regression:
+
+- `FCNRegressor` - Fully convolutional network
+- `ResNetRegressor` - Residual blocks with skip connections
+- `InceptionTimeRegressor` - Multi-scale inception modules
+- `TimeCNNRegressor` - Standard CNN architecture
+- `RecurrentRegressor` - RNN/LSTM/GRU variants
+- `MLPRegressor` - Multi-layer perceptron
+- `EncoderRegressor` - Generic encoder wrapper
+- `LITERegressor` - Lightweight inception time ensemble
+- `DisjointCNNRegressor` - Specialized CNN architecture
+
+**Use when**: Large datasets, complex patterns, or need feature learning.
+
+## Distance-Based Regressors
+
+k-nearest neighbors with temporal distance metrics:
+
+- `KNeighborsTimeSeriesRegressor` - k-NN with DTW, LCSS, ERP, or other distances
+
+**Use when**: Small datasets, local similarity patterns, or interpretable predictions.
+
+## Feature-Based Regressors
+
+Extract statistical features before regression:
+
+- `Catch22Regressor` - 22 canonical time-series characteristics
+- `FreshPRINCERegressor` - Pipeline combining multiple feature extractors
+- `SummaryRegressor` - Summary statistics features
+- `TSFreshRegressor` - Automated tsfresh feature extraction
+
+**Use when**: Need interpretable features or domain-specific feature engineering.
+
+## Hybrid Regressors
+
+Combine multiple approaches:
+
+- `RISTRegressor` - Randomized Interval-Shapelet Transformation
+
+**Use when**: Benefit from combining interval and shapelet methods.
+
+## Interval-Based Regressors
+
+Extract features from time intervals:
+
+- `CanonicalIntervalForestRegressor` - Random intervals with decision trees
+- `DrCIFRegressor` - Diverse Representation CIF
+- `TimeSeriesForestRegressor` - Random interval ensemble
+- `RandomIntervalRegressor` - Simple interval-based approach
+- `RandomIntervalSpectralEnsembleRegressor` - Spectral interval features
+- `QUANTRegressor` - Quantile-based interval features
+
+**Use when**: Predictive patterns occur in specific time windows.
+
+## Shapelet-Based Regressors
+
+Use discriminative subsequences for prediction:
+
+- `RDSTRegressor` - Random Dilated Shapelet Transform
+
+**Use when**: Need phase-invariant discriminative patterns.
+
+## Composition Tools
+
+Build custom regression pipelines:
+
+- `RegressorPipeline` - Chain transformers with regressors
+- `RegressorEnsemble` - Weighted ensemble with learnable weights
+- `SklearnRegressorWrapper` - Adapt sklearn regressors for time series
+
+## Utilities
+
+- `DummyRegressor` - Baseline strategies (mean, median)
+- `BaseRegressor` - Abstract base for custom regressors
+- `BaseDeepRegressor` - Base for deep learning regressors
+
+## Quick Start
+
+```python
+from aeon.regression.convolution_based import RocketRegressor
+from aeon.datasets import load_regression
+
+# Load data
+X_train, y_train = load_regression("Covid3Month", split="train")
+X_test, y_test = load_regression("Covid3Month", split="test")
+
+# Train and predict
+reg = RocketRegressor()
+reg.fit(X_train, y_train)
+predictions = reg.predict(X_test)
+```
+
+## Algorithm Selection
+
+- **Speed priority**: MiniRocketRegressor
+- **Accuracy priority**: InceptionTimeRegressor, MultiRocketHydraRegressor
+- **Interpretability**: Catch22Regressor, SummaryRegressor
+- **Small data**: KNeighborsTimeSeriesRegressor
+- **Large data**: Deep learning regressors, ROCKET variants
+- **Interval patterns**: DrCIFRegressor, CanonicalIntervalForestRegressor

skills/aeon/references/segmentation.md

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+# Time Series Segmentation
+
+Aeon provides algorithms to partition time series into regions with distinct characteristics, identifying change points and boundaries.
+
+## Segmentation Algorithms
+
+### Binary Segmentation
+- `BinSegmenter` - Recursive binary segmentation
+  - Iteratively splits series at most significant change points
+  - Parameters: `n_segments`, `cost_function`
+  - **Use when**: Known number of segments, hierarchical structure
+
+### Classification-Based
+- `ClaSPSegmenter` - Classification Score Profile
+  - Uses classification performance to identify boundaries
+  - Discovers segments where classification distinguishes neighbors
+  - **Use when**: Segments have different temporal patterns
+
+### Fast Pattern-Based
+- `FLUSSSegmenter` - Fast Low-cost Unipotent Semantic Segmentation
+  - Efficient semantic segmentation using arc crossings
+  - Based on matrix profile
+  - **Use when**: Large time series, need speed and pattern discovery
+
+### Information Theory
+- `InformationGainSegmenter` - Information gain maximization
+  - Finds boundaries maximizing information gain
+  - **Use when**: Statistical differences between segments
+
+### Gaussian Modeling
+- `GreedyGaussianSegmenter` - Greedy Gaussian approximation
+  - Models segments as Gaussian distributions
+  - Incrementally adds change points
+  - **Use when**: Segments follow Gaussian distributions
+
+### Hierarchical Agglomerative
+- `EAggloSegmenter` - Bottom-up merging approach
+  - Estimates change points via agglomeration
+  - **Use when**: Want hierarchical segmentation structure
+
+### Hidden Markov Models
+- `HMMSegmenter` - HMM with Viterbi decoding
+  - Probabilistic state-based segmentation
+  - **Use when**: Segments represent hidden states
+
+### Dimensionality-Based
+- `HidalgoSegmenter` - Heterogeneous Intrinsic Dimensionality Algorithm
+  - Detects changes in local dimensionality
+  - **Use when**: Dimensionality shifts between segments
+
+### Baseline
+- `RandomSegmenter` - Random change point generation
+  - **Use when**: Need null hypothesis baseline
+
+## Quick Start
+
+```python
+from aeon.segmentation import ClaSPSegmenter
+import numpy as np
+
+# Create time series with regime changes
+y = np.concatenate([
+    np.sin(np.linspace(0, 10, 100)),      # Segment 1
+    np.cos(np.linspace(0, 10, 100)),      # Segment 2
+    np.sin(2 * np.linspace(0, 10, 100))   # Segment 3
+])
+
+# Segment the series
+segmenter = ClaSPSegmenter()
+change_points = segmenter.fit_predict(y)
+
+print(f"Detected change points: {change_points}")
+```
+
+## Output Format
+
+Segmenters return change point indices:
+
+```python
+# change_points = [100, 200]  # Boundaries between segments
+# This divides series into: [0:100], [100:200], [200:end]
+```
+
+## Algorithm Selection
+
+- **Speed priority**: FLUSSSegmenter, BinSegmenter
+- **Accuracy priority**: ClaSPSegmenter, HMMSegmenter
+- **Known segment count**: BinSegmenter with n_segments parameter
+- **Unknown segment count**: ClaSPSegmenter, InformationGainSegmenter
+- **Pattern changes**: FLUSSSegmenter, ClaSPSegmenter
+- **Statistical changes**: InformationGainSegmenter, GreedyGaussianSegmenter
+- **State transitions**: HMMSegmenter
+
+## Common Use Cases
+
+### Regime Change Detection
+Identify when time series behavior fundamentally changes:
+
+```python
+from aeon.segmentation import InformationGainSegmenter
+
+segmenter = InformationGainSegmenter(k=3)  # Up to 3 change points
+change_points = segmenter.fit_predict(stock_prices)
+```
+
+### Activity Segmentation
+Segment sensor data into activities:
+
+```python
+from aeon.segmentation import ClaSPSegmenter
+
+segmenter = ClaSPSegmenter()
+boundaries = segmenter.fit_predict(accelerometer_data)
+```
+
+### Seasonal Boundary Detection
+Find season transitions in time series:
+
+```python
+from aeon.segmentation import HMMSegmenter
+
+segmenter = HMMSegmenter(n_states=4)  # 4 seasons
+segments = segmenter.fit_predict(temperature_data)
+```
+
+## Evaluation Metrics
+
+Use segmentation quality metrics:
+
+```python
+from aeon.benchmarking.metrics.segmentation import (
+    count_error,
+    hausdorff_error
+)
+
+# Count error: difference in number of change points
+count_err = count_error(y_true, y_pred)
+
+# Hausdorff: maximum distance between predicted and true points
+hausdorff_err = hausdorff_error(y_true, y_pred)
+```
+
+## Best Practices
+
+1. **Normalize data**: Ensures change detection not dominated by scale
+2. **Choose appropriate metric**: Different algorithms optimize different criteria
+3. **Validate segments**: Visualize to verify meaningful boundaries
+4. **Handle noise**: Consider smoothing before segmentation
+5. **Domain knowledge**: Use expected segment count if known
+6. **Parameter tuning**: Adjust sensitivity parameters (thresholds, penalties)
+
+## Visualization
+
+```python
+import matplotlib.pyplot as plt
+
+plt.figure(figsize=(12, 4))
+plt.plot(y, label='Time Series')
+for cp in change_points:
+    plt.axvline(cp, color='r', linestyle='--', label='Change Point')
+plt.legend()
+plt.show()
+```

skills/aeon/references/similarity_search.md

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+# Similarity Search
+
+Aeon provides tools for finding similar patterns within and across time series, including subsequence search, motif discovery, and approximate nearest neighbors.
+
+## Subsequence Nearest Neighbors (SNN)
+
+Find most similar subsequences within a time series.
+
+### MASS Algorithm
+- `MassSNN` - Mueen's Algorithm for Similarity Search
+  - Fast normalized cross-correlation for similarity
+  - Computes distance profile efficiently
+  - **Use when**: Need exact nearest neighbor distances, large series
+
+### STOMP-Based Motif Discovery
+- `StompMotif` - Discovers recurring patterns (motifs)
+  - Finds top-k most similar subsequence pairs
+  - Based on matrix profile computation
+  - **Use when**: Want to discover repeated patterns
+
+### Brute Force Baseline
+- `DummySNN` - Exhaustive distance computation
+  - Computes all pairwise distances
+  - **Use when**: Small series, need exact baseline
+
+## Collection-Level Search
+
+Find similar time series across collections.
+
+### Approximate Nearest Neighbors (ANN)
+- `RandomProjectionIndexANN` - Locality-sensitive hashing
+  - Uses random projections with cosine similarity
+  - Builds index for fast approximate search
+  - **Use when**: Large collection, speed more important than exactness
+
+## Quick Start: Motif Discovery
+
+```python
+from aeon.similarity_search import StompMotif
+import numpy as np
+
+# Create time series with repeated patterns
+pattern = np.sin(np.linspace(0, 2*np.pi, 50))
+y = np.concatenate([
+    pattern + np.random.normal(0, 0.1, 50),
+    np.random.normal(0, 1, 100),
+    pattern + np.random.normal(0, 0.1, 50),
+    np.random.normal(0, 1, 100)
+])
+
+# Find top-3 motifs
+motif_finder = StompMotif(window_size=50, k=3)
+motifs = motif_finder.fit_predict(y)
+
+# motifs contains indices of motif occurrences
+for i, (idx1, idx2) in enumerate(motifs):
+    print(f"Motif {i+1} at positions {idx1} and {idx2}")
+```
+
+## Quick Start: Subsequence Search
+
+```python
+from aeon.similarity_search import MassSNN
+import numpy as np
+
+# Time series to search within
+y = np.sin(np.linspace(0, 20, 500))
+
+# Query subsequence
+query = np.sin(np.linspace(0, 2, 50))
+
+# Find nearest subsequences
+searcher = MassSNN()
+distances = searcher.fit_transform(y, query)
+
+# Find best match
+best_match_idx = np.argmin(distances)
+print(f"Best match at index {best_match_idx}")
+```
+
+## Quick Start: Approximate NN on Collections
+
+```python
+from aeon.similarity_search import RandomProjectionIndexANN
+from aeon.datasets import load_classification
+
+# Load time series collection
+X_train, _ = load_classification("GunPoint", split="train")
+
+# Build index
+ann = RandomProjectionIndexANN(n_projections=8, n_bits=4)
+ann.fit(X_train)
+
+# Find approximate nearest neighbors
+query = X_train[0]
+neighbors, distances = ann.kneighbors(query, k=5)
+```
+
+## Matrix Profile
+
+The matrix profile is a fundamental data structure for many similarity search tasks:
+
+- **Distance Profile**: Distances from a query to all subsequences
+- **Matrix Profile**: Minimum distance for each subsequence to any other
+- **Motif**: Pair of subsequences with minimum distance
+- **Discord**: Subsequence with maximum minimum distance (anomaly)
+
+```python
+from aeon.similarity_search import StompMotif
+
+# Compute matrix profile and find motifs/discords
+mp = StompMotif(window_size=50)
+mp.fit(y)
+
+# Access matrix profile
+profile = mp.matrix_profile_
+profile_indices = mp.matrix_profile_index_
+
+# Find discords (anomalies)
+discord_idx = np.argmax(profile)
+```
+
+## Algorithm Selection
+
+- **Exact subsequence search**: MassSNN
+- **Motif discovery**: StompMotif
+- **Anomaly detection**: Matrix profile (see anomaly_detection.md)
+- **Fast approximate search**: RandomProjectionIndexANN
+- **Small data**: DummySNN for exact results
+
+## Use Cases
+
+### Pattern Matching
+Find where a pattern occurs in a long series:
+
+```python
+# Find heartbeat pattern in ECG data
+searcher = MassSNN()
+distances = searcher.fit_transform(ecg_data, heartbeat_pattern)
+occurrences = np.where(distances < threshold)[0]
+```
+
+### Motif Discovery
+Identify recurring patterns:
+
+```python
+# Find repeated behavioral patterns
+motif_finder = StompMotif(window_size=100, k=5)
+motifs = motif_finder.fit_predict(activity_data)
+```
+
+### Time Series Retrieval
+Find similar time series in database:
+
+```python
+# Build searchable index
+ann = RandomProjectionIndexANN()
+ann.fit(time_series_database)
+
+# Query for similar series
+neighbors = ann.kneighbors(query_series, k=10)
+```
+
+## Best Practices
+
+1. **Window size**: Critical parameter for subsequence methods
+   - Too small: Captures noise
+   - Too large: Misses fine-grained patterns
+   - Rule of thumb: 10-20% of series length
+
+2. **Normalization**: Most methods assume z-normalized subsequences
+   - Handles amplitude variations
+   - Focus on shape similarity
+
+3. **Distance metrics**: Different metrics for different needs
+   - Euclidean: Fast, shape-based
+   - DTW: Handles temporal warping
+   - Cosine: Scale-invariant
+
+4. **Exclusion zone**: For motif discovery, exclude trivial matches
+   - Typically set to 0.5-1.0 × window_size
+   - Prevents finding overlapping occurrences
+
+5. **Performance**:
+   - MASS is O(n log n) vs O(n²) brute force
+   - ANN trades accuracy for speed
+   - GPU acceleration available for some methods

skills/aeon/references/transformations.md

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+# Transformations
+
+Aeon provides extensive transformation capabilities for preprocessing, feature extraction, and representation learning from time series data.
+
+## Transformation Types
+
+Aeon distinguishes between:
+- **CollectionTransformers**: Transform multiple time series (collections)
+- **SeriesTransformers**: Transform individual time series
+
+## Collection Transformers
+
+### Convolution-Based Feature Extraction
+
+Fast, scalable feature generation using random kernels:
+
+- `RocketTransformer` - Random convolutional kernels
+- `MiniRocketTransformer` - Simplified ROCKET for speed
+- `MultiRocketTransformer` - Enhanced ROCKET variant
+- `HydraTransformer` - Multi-resolution dilated convolutions
+- `MultiRocketHydraTransformer` - Combines ROCKET and Hydra
+- `ROCKETGPU` - GPU-accelerated variant
+
+**Use when**: Need fast, scalable features for any ML algorithm, strong baseline performance.
+
+### Statistical Feature Extraction
+
+Domain-agnostic features based on time series characteristics:
+
+- `Catch22` - 22 canonical time-series characteristics
+- `TSFresh` - Comprehensive automated feature extraction (100+ features)
+- `TSFreshRelevant` - Feature extraction with relevance filtering
+- `SevenNumberSummary` - Descriptive statistics (mean, std, quantiles)
+
+**Use when**: Need interpretable features, domain-agnostic approach, or feeding traditional ML.
+
+### Dictionary-Based Representations
+
+Symbolic approximations for discrete representations:
+
+- `SAX` - Symbolic Aggregate approXimation
+- `PAA` - Piecewise Aggregate Approximation
+- `SFA` - Symbolic Fourier Approximation
+- `SFAFast` - Optimized SFA
+- `SFAWhole` - SFA on entire series (no windowing)
+- `BORF` - Bag-of-Receptive-Fields
+
+**Use when**: Need discrete/symbolic representation, dimensionality reduction, interpretability.
+
+### Shapelet-Based Features
+
+Discriminative subsequence extraction:
+
+- `RandomShapeletTransform` - Random discriminative shapelets
+- `RandomDilatedShapeletTransform` - Dilated shapelets for multi-scale
+- `SAST` - Scalable And Accurate Subsequence Transform
+- `RSAST` - Randomized SAST
+
+**Use when**: Need interpretable discriminative patterns, phase-invariant features.
+
+### Interval-Based Features
+
+Statistical summaries from time intervals:
+
+- `RandomIntervals` - Features from random intervals
+- `SupervisedIntervals` - Supervised interval selection
+- `QUANTTransformer` - Quantile-based interval features
+
+**Use when**: Predictive patterns localized to specific windows.
+
+### Preprocessing Transformations
+
+Data preparation and normalization:
+
+- `MinMaxScaler` - Scale to [0, 1] range
+- `Normalizer` - Z-normalization (zero mean, unit variance)
+- `Centerer` - Center to zero mean
+- `SimpleImputer` - Fill missing values
+- `DownsampleTransformer` - Reduce temporal resolution
+- `Tabularizer` - Convert time series to tabular format
+
+**Use when**: Need standardization, missing value handling, format conversion.
+
+### Specialized Transformations
+
+Advanced analysis methods:
+
+- `MatrixProfile` - Computes distance profiles for pattern discovery
+- `DWTTransformer` - Discrete Wavelet Transform
+- `AutocorrelationFunctionTransformer` - ACF computation
+- `Dobin` - Distance-based Outlier BasIs using Neighbors
+- `SignatureTransformer` - Path signature methods
+- `PLATransformer` - Piecewise Linear Approximation
+
+### Class Imbalance Handling
+
+- `ADASYN` - Adaptive Synthetic Sampling
+- `SMOTE` - Synthetic Minority Over-sampling
+- `OHIT` - Over-sampling with Highly Imbalanced Time series
+
+**Use when**: Classification with imbalanced classes.
+
+### Pipeline Composition
+
+- `CollectionTransformerPipeline` - Chain multiple transformers
+
+## Series Transformers
+
+Transform individual time series (e.g., for preprocessing in forecasting).
+
+### Statistical Analysis
+
+- `AutoCorrelationSeriesTransformer` - Autocorrelation
+- `StatsModelsACF` - ACF using statsmodels
+- `StatsModelsPACF` - Partial autocorrelation
+
+### Smoothing and Filtering
+
+- `ExponentialSmoothing` - Exponentially weighted moving average
+- `MovingAverage` - Simple or weighted moving average
+- `SavitzkyGolayFilter` - Polynomial smoothing
+- `GaussianFilter` - Gaussian kernel smoothing
+- `BKFilter` - Baxter-King bandpass filter
+- `DiscreteFourierApproximation` - Fourier-based filtering
+
+**Use when**: Need noise reduction, trend extraction, or frequency filtering.
+
+### Dimensionality Reduction
+
+- `PCASeriesTransformer` - Principal component analysis
+- `PlASeriesTransformer` - Piecewise Linear Approximation
+
+### Transformations
+
+- `BoxCoxTransformer` - Variance stabilization
+- `LogTransformer` - Logarithmic scaling
+- `ClaSPTransformer` - Classification Score Profile
+
+### Pipeline Composition
+
+- `SeriesTransformerPipeline` - Chain series transformers
+
+## Quick Start: Feature Extraction
+
+```python
+from aeon.transformations.collection.convolution_based import RocketTransformer
+from aeon.classification.sklearn import RotationForest
+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")
+
+# Extract ROCKET features
+rocket = RocketTransformer()
+X_train_features = rocket.fit_transform(X_train)
+X_test_features = rocket.transform(X_test)
+
+# Use with any sklearn classifier
+clf = RotationForest()
+clf.fit(X_train_features, y_train)
+accuracy = clf.score(X_test_features, y_test)
+```
+
+## Quick Start: Preprocessing Pipeline
+
+```python
+from aeon.transformations.collection import (
+    MinMaxScaler,
+    SimpleImputer,
+    CollectionTransformerPipeline
+)
+
+# Build preprocessing pipeline
+pipeline = CollectionTransformerPipeline([
+    ('imputer', SimpleImputer(strategy='mean')),
+    ('scaler', MinMaxScaler())
+])
+
+X_transformed = pipeline.fit_transform(X_train)
+```
+
+## Quick Start: Series Smoothing
+
+```python
+from aeon.transformations.series import MovingAverage
+
+# Smooth individual time series
+smoother = MovingAverage(window_size=5)
+y_smoothed = smoother.fit_transform(y)
+```
+
+## Algorithm Selection
+
+### For Feature Extraction:
+- **Speed + Performance**: MiniRocketTransformer
+- **Interpretability**: Catch22, TSFresh
+- **Dimensionality reduction**: PAA, SAX, PCA
+- **Discriminative patterns**: Shapelet transforms
+- **Comprehensive features**: TSFresh (with longer runtime)
+
+### For Preprocessing:
+- **Normalization**: Normalizer, MinMaxScaler
+- **Smoothing**: MovingAverage, SavitzkyGolayFilter
+- **Missing values**: SimpleImputer
+- **Frequency analysis**: DWTTransformer, Fourier methods
+
+### For Symbolic Representation:
+- **Fast approximation**: PAA
+- **Alphabet-based**: SAX
+- **Frequency-based**: SFA, SFAFast
+
+## Best Practices
+
+1. **Fit on training data only**: Avoid data leakage
+   ```python
+   transformer.fit(X_train)
+   X_train_tf = transformer.transform(X_train)
+   X_test_tf = transformer.transform(X_test)
+   ```
+
+2. **Pipeline composition**: Chain transformers for complex workflows
+   ```python
+   pipeline = CollectionTransformerPipeline([
+       ('imputer', SimpleImputer()),
+       ('scaler', Normalizer()),
+       ('features', RocketTransformer())
+   ])
+   ```
+
+3. **Feature selection**: TSFresh can generate many features; consider selection
+   ```python
+   from sklearn.feature_selection import SelectKBest
+   selector = SelectKBest(k=100)
+   X_selected = selector.fit_transform(X_features, y)
+   ```
+
+4. **Memory considerations**: Some transformers memory-intensive on large datasets
+   - Use MiniRocket instead of ROCKET for speed
+   - Consider downsampling for very long series
+   - Use ROCKETGPU for GPU acceleration
+
+5. **Domain knowledge**: Choose transformations matching domain:
+   - Periodic data: Fourier-based methods
+   - Noisy data: Smoothing filters
+   - Spike detection: Wavelet transforms