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

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