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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