[DOC] Anomaly Detection Overview Notebook (#2446)

* AB overview nb

* Overview NB

* Anomaly Detection Overview

* First draft

* git status

* API updated

* Performance metrics

* use correct performance metrics

docs/api_reference/anomaly_detection.rst

@@ -3,58 +3,11 @@
 Anomaly Detection
 =================
 
-Time Series Anomaly Detection aims at discovering regions of a time series that in
-some way are not representative of the underlying generative process.
-The :mod:`aeon.anomaly_detection` module contains algorithms and tools
-for time series anomaly detection. The detectors have different capabilities that can
-be grouped into the following categories, where ``m`` is the number of time points and
-``d`` is the number of channels for a time series:
+The :mod:`aeon.anomaly_detection` module contains algorithms and composition tools for time series classification.
 
-Input data format (one of the following):
-    Univariate series (default):
-        Example: :class:`~aeon.anomaly_detection.MERLIN`.
-
-        - np.ndarray, shape ``(m,)``, ``(m, 1)`` or ``(1, m)`` depending on axis.
-        - pd.DataFrame, shape ``(m, 1)`` or ``(1, m)`` depending on axis.
-        - pd.Series, shape ``(m,)``.
-    Multivariate series:
-        Example: :class:`~aeon.anomaly_detection.KMeansAD`.
-
-        - np.ndarray array, shape ``(m, d)`` or ``(d, m)`` depending on axis.
-        - pd.DataFrame ``(m, d)`` or ``(d, m)`` depending on axis.
-
-Output data format (one of the following):
-    Anomaly scores (default):
-        np.ndarray, shape ``(m,)`` of type float. For each point of the input time
-        series, the anomaly score is a float value indicating the degree of
-        anomalousness. The higher the score, the more anomalous the point. The
-        detectors return raw anomaly scores that are not normalized.
-        Example: :class:`~aeon.anomaly_detection.PyODAdapter`.
-    Binary classification:
-        np.ndarray, shape ``(m,)`` of type bool or int. For each point of the input
-        time series, the output is a boolean or integer value indicating whether the
-        point is anomalous (``True``/``1``) or not (``False``/``0``).
-        Example: :class:`~aeon.anomaly_detection.STRAY`.
-
-Detector learning types:
-    Unsupervised (default):
-        Unsupervised detectors do not require any training data and can directly be
-        used on the target time series. You would usually call the ``fit_predict``
-        method on these detectors.
-        Example: :class:`~aeon.anomaly_detection.DWT_MLEAD`.
-    Semi-supervised:
-        Semi-supervised detectors require a training step on a time series without
-        anomalies (normal behaving time series). The target value ``y`` would
-        consist of only zeros. You would usually first call the ``fit`` method on the
-        training time series and then the ``predict`` method on your target time series.
-        Example: :class:`~aeon.anomaly_detection.KMeansAD`.
-    Supervised:
-        Supervised detectors require a training step on a time series with known
-        anomalies (anomalies should be present and must be annotated). The detector
-        implements the ``fit`` method, and the target value ``y`` consists of zeros
-        and ones; ones indicating points of an anomaly. You would usually first call
-        the ``fit`` method on the training data and then the ``predict`` method on your
-        target time series.
+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