def show_anomalies(
        self,
        series: TimeSeries,
        anomalies: TimeSeries = None,
        names_of_scorers: Union[str, Sequence[str]] = None,
        title: str = None,
        metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,
        **score_kwargs,
    ):
        """Plot the results of the anomaly model.

        Computes the score on the given series input and shows the different anomaly scores with respect to time.

        The plot will be composed of the following:

        - the series itself with the output of the forecasting model.
        - the anomaly score for each scorer. The scorers with different windows will be separated.
        - the actual anomalies, if given.

        It is possible to:

        - add a title to the figure with the parameter `title`
        - give personalized names for the scorers with `names_of_scorers`
        - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are provided.

        Parameters
        ----------
        series
            The series to visualize anomalies from.
        anomalies
            The ground truth of the anomalies (1 if it is an anomaly and 0 if not).
        names_of_scorers
            Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model.
        title
            Title of the figure.
        metric
            Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
            Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
            Default: "AUC_ROC".
        score_kwargs
            parameters for the `score()` method.
        """
        return super().show_anomalies(
            series=series,
            anomalies=anomalies,
            predict_kwargs=None,
            names_of_scorers=names_of_scorers,
            title=title,
            metric=metric,
            **score_kwargs,
        )
Plot the results of the anomaly model.

        Computes the score on the given series input and shows the different anomaly scores with respect to time.

        The plot will be composed of the following:

        - the series itself with the output of the forecasting model.
        - the anomaly score for each scorer. The scorers with different windows will be separated.
        - the actual anomalies, if given.

        It is possible to:

        - add a title to the figure with the parameter `title`
        - give personalized names for the scorers with `names_of_scorers`
        - show the results of a metric for each anomaly score (AUC_ROC or AUC_PR), if the actual anomalies are provided.

        Parameters
        ----------
        series
            The series to visualize anomalies from.
        anomalies
            The ground truth of the anomalies (1 if it is an anomaly and 0 if not).
        names_of_scorers
            Name of the scores. Must be a list of length equal to the number of scorers in the anomaly_model.
        title
            Title of the figure.
        metric
            Optionally, the name of the metric function to use. Must be one of "AUC_ROC" (Area Under the
            Receiver Operating Characteristic Curve) and "AUC_PR" (Average Precision from scores).
            Default: "AUC_ROC".
        score_kwargs
            parameters for the `score()` method.
        
darts/ad/anomaly_model/filtering_am.py