get_anomalies_prediction_interval(ts: etna.datasets.tsdataset.TSDataset, model: Union[Type[ProphetModel], Type[SARIMAXModel]], interval_width: float = 0.95, in_column: str = 'target', **model_params) Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]][source]

Get point outliers in time series using prediction intervals (estimation model-based method).

Outliers are all points out of the prediction interval predicted with the model.

  • ts (etna.datasets.tsdataset.TSDataset) – dataset with timeseries data(should contains all the necessary features).

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

  • interval_width (float) – the significance level for the prediction interval. By default a 95% prediction interval is taken.

  • in_column (str) –

    column to analyze

    • If it is set to “target”, then all data will be used for prediction.

    • Otherwise, only column data will be used.


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

Return type

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


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