Anomaly Detection

Time Series Anomaly Detection aims at discovering regions of a time series that in some way not representative of the underlying generative process. The aeon.anomaly_detection module contains algorithms and tools for time series anomaly detection.

DWT_MLEAD([start_level, ...])	DWT-MLEAD anomaly detector.
KMeansAD([n_clusters, window_size, stride, ...])	KMeans anomaly detector.
MERLIN([min_length, max_length, max_iterations])	MERLIN anomaly detector.
PyODAdapter(pyod_model[, window_size, stride])	Adapter for PyOD anomaly detection models to be used in the Aeon framework.
STRAY([alpha, k, knn_algorithm, p, ...])	STRAY: robust anomaly detection in data streams with concept drift.