DifferencingTransform

class DifferencingTransform(in_column: str, period: int = 1, order: int = 1, inplace: bool = True, out_column: Optional[str] = None)

Bases: etna.transforms.base.ReversibleTransform

Calculate a time series differences.

During fit this transform can work with NaNs at the beginning of the segment, but fails when meets NaN inside the segment. During transform and inverse_transform there is no special treatment of NaNs.

Notes

To understand how transform works we recommend: Stationarity and Differencing

Create instance of DifferencingTransform.

Parameters

• in_column (str) – name of processed column

• period (int) – number of steps back to calculate the difference with, it should be >= 1

• order (int) – number of differences to make, it should be >= 1

• inplace (bool) –

  • if True, apply transformation inplace to in_column,

  • if False, add transformed column to dataset

• out_column (Optional[str]) –

  • if set, name of added column, the final name will be ‘{out_column}’;

  • if isn’t set, name will be based on self.__repr__()

Raises

• ValueError: – if period is not integer >= 1

• ValueError: – if order is not integer >= 1

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.

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.math.differencing.DifferencingTransform

Fit the transform.

Parameters

ts (etna.datasets.tsdataset.TSDataset) –

Return type

etna.transforms.math.differencing.DifferencingTransform

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 order parameter. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

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