SpecialDaysTransform

class SpecialDaysTransform(find_special_weekday: bool = True, find_special_month_day: bool = True)

Bases: etna.transforms.base.IrreversiblePerSegmentWrapper, etna.transforms.base.FutureMixin

SpecialDaysTransform generates series that indicates is weekday/monthday is special in given dataframe.

Creates columns ‘anomaly_weekdays’ and ‘anomaly_monthdays’.

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 SpecialDaysTransform.

Parameters

• find_special_weekday (bool) – flag, if True, find special weekdays in transform

• find_special_month_day (bool) – flag, if True, find special monthdays in transform

Raises

ValueError: – if all the modes are False

Inherited-members

Methods

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.

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Return type

List[str]

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

Get default grid for tuning hyperparameters.

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

There are no restrictions on all False values for the flags.

Returns

Grid to tune.

Return type

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