Anomaly Detection

The aeon.anomaly_detection module contains algorithms and composition tools for time series classification.

All detectors in aeon can be listed using the aeon.utils.discovery.all_estimators utility, using estimator_types="anomaly-detector", optionally filtered by tags. Valid tags can be listed by calling the function aeon.utils.discovery.all_tags_for_estimator.

Each detector in this module specifies its supported input data format, output data format, and learning type as an overview table in its documentation. Some detectors support multiple learning types.

Detectors

CBLOF([n_clusters, clustering_estimator, ...])	CBLOF for Anomaly Detection.
COPOD([n_jobs, window_size, stride])	COPOD for anomaly detection.
DWT_MLEAD([start_level, ...])	DWT-MLEAD anomaly detector.
IsolationForest([n_estimators, max_samples, ...])	Isolation Forest for anomaly detection.
KMeansAD([n_clusters, window_size, stride, ...])	KMeans anomaly detector.
LeftSTAMPi([window_size, n_init_train, ...])	LeftSTAMPi anomaly detector.
LOF([n_neighbors, algorithm, leaf_size, ...])	Local Outlier Factor (LOF) algorithm for anomaly detection.
MERLIN([min_length, max_length, max_iterations])	MERLIN anomaly detector.
OneClassSVM([nu, kernel, degree, gamma, ...])	OneClassSVM for anomaly detection.
PyODAdapter(pyod_model[, window_size, stride])	Adapter for PyOD anomaly detection models to be used in the Aeon framework.
ROCKAD([n_estimators, n_kernels, normalise, ...])	ROCKET-based Anomaly Detector (ROCKAD).
STOMP([window_size, ignore_trivial, ...])	STOMP anomaly detector.
STRAY([alpha, k, knn_algorithm, p, ...])	STRAY: robust anomaly detection in data streams with concept drift.

Base

BaseAnomalyDetector(axis)

Base class for anomaly detection algorithms.