anomaly_detection.rst

Anomaly Detection

The :mod:`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.

Note

Not all algorithm families are currently implemented. The documentation includes placeholders for planned categories which will be supported in future.

Distance-based

.. currentmodule:: aeon.anomaly_detection.distance_based

.. autosummary::
    :toctree: auto_generated/
    :template: class.rst

    CBLOF
    KMeansAD
    LeftSTAMPi
    LOF
    MERLIN
    OneClassSVM
    STOMP

Distribution-based

.. currentmodule:: aeon.anomaly_detection.distribution_based

.. autosummary::
    :toctree: auto_generated/
    :template: class.rst

    COPOD
    DWT_MLEAD

Encoding-based

The algorithms for this family are not implemented yet.

Forecasting-based

The algorithms for this family are not implemented yet.

Outlier-Detection

.. currentmodule:: aeon.anomaly_detection.outlier_detection

.. autosummary::
    :toctree: auto_generated/
    :template: class.rst

    IsolationForest
    PyODAdapter
    STRAY

Reconstruction-based

The algorithms for this family are not implemented yet.

Whole-Series

.. currentmodule:: aeon.anomaly_detection.whole_series

.. autosummary::
    :toctree: auto_generated/
    :template: class.rst

    ROCKAD

Base

.. currentmodule:: aeon.anomaly_detection.base

.. autosummary::
    :toctree: auto_generated/
    :template: class.rst

    BaseAnomalyDetector