PredictionIntervalOutliersTransform

class PredictionIntervalOutliersTransform(in_column: str, model: Union[Type[etna.models.prophet.ProphetModel], Type[etna.models.sarimax.SARIMAXModel]], interval_width: float = 0.95, **model_kwargs)

Bases: etna.transforms.outliers.base.OutliersTransform

Transform that uses get_anomalies_prediction_interval() to find anomalies in data.

Create instance of PredictionIntervalOutliersTransform.

Parameters

• in_column (str) – name of processed column

• model (Union[Type[ProphetModel], Type[SARIMAXModel]]) – model for prediction interval estimation

• interval_width (float) – width of the prediction interval

Notes

For not “target” column only column data will be used for learning.

Inherited-members

Parameters

• in_column (str) –

• model (Union[Type[ProphetModel], Type[SARIMAXModel]]) –

• interval_width (float) –

Methods

detect_outliers(ts)	Call get_anomalies_prediction_interval() function with self parameters.
fit(df)	Find outliers using detection method.
fit_transform(df)	May be reimplemented.
inverse_transform(df)	Inverse transformation.
transform(df)	Replace found outliers with NaNs.

detect_outliers(ts: etna.datasets.tsdataset.TSDataset) → Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]]

Call get_anomalies_prediction_interval() function with self parameters.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – dataset to process

Returns

dict of outliers in format {segment: [outliers_timestamps]}

Return type

Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]]