DeseasonalityTransform

class DeseasonalityTransform(in_column: str, period: int, model: Literal['additive', 'multiplicative'] = 'additive')

Bases: etna.transforms.base.ReversiblePerSegmentWrapper

Transform that uses statsmodels.tsa.seasonal.seasonal_decompose() to subtract seasonal component from the data.

Warning

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

Init DeseasonalityTransform.

Parameters

• in_column (str) – name of processed column

• period (int) – size of seasonality

• model (Literal['additive', 'multiplicative']) – ‘additive’ (Y[t] = T[t] + S[t] + e[t], default option) or ‘multiplicative’ (Y[t] = T[t] * S[t] * e[t])

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: model. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

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