MedianOutliersTransform

class MedianOutliersTransform(in_column: str, window_size: int = 10, alpha: float = 3)

Bases: etna.transforms.outliers.base.OutliersTransform

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

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Create instance of MedianOutliersTransform.

Parameters

• in_column (str) – name of processed column

• window_size (int) – number of points in the window

• alpha (float) – coefficient for determining the threshold

Inherited-members

Methods

detect_outliers(ts)	Call get_anomalies_median() function with self parameters.
fit(ts)	Fit the transform.
fit_transform(ts)	Fit and transform TSDataset.
get_regressors_info()	Return the list with regressors created by the transform.
inverse_transform(ts)	Inverse transform TSDataset.
load(path)	Load an object.
params_to_tune()	Get default grid for tuning hyperparameters.
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.
transform(ts)	Transform TSDataset inplace.

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

Call get_anomalies_median() 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]]

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get default grid for tuning hyperparameters.

This grid tunes parameters: window_size, alpha. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]