etna.transforms.DifferencingTransform#

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

Bases: 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 (str | None) –

    • 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

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.

Attributes

This class stores its __init__ parameters as attributes.

fit(ts: TSDataset) → DifferencingTransform[source]#

Fit the transform.

Parameters:

ts (TSDataset) –

Return type:

DifferencingTransform

fit_transform(ts: TSDataset) → TSDataset[source]#

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters:

ts (TSDataset) – TSDataset to transform.

Returns:

Transformed TSDataset.

Return type:

TSDataset

get_regressors_info() → List[str][source]#

Return the list with regressors created by the transform.

Return type:

List[str]

inverse_transform(ts: TSDataset) → TSDataset[source]#

Inverse transform TSDataset.

Apply the _inverse_transform method.

Parameters:

ts (TSDataset) – TSDataset to be inverse transformed.

Returns:

TSDataset after applying inverse transformation.

Return type:

TSDataset

classmethod load(path: Path) → Self[source]#

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:

path (Path) – Path to load object from.

Returns:

Loaded object.

Return type:

Self

params_to_tune() → Dict[str, BaseDistribution][source]#

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, BaseDistribution]

save(path: Path)[source]#

Save the object.

Parameters:

path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:

**params (dict) – Estimator parameters

Returns:

New instance with changed parameters

Return type:

Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()[source]#

Collect all information about etna object in dict.

transform(ts: TSDataset) → TSDataset[source]#

Transform TSDataset inplace.

Parameters:

ts (TSDataset) – Dataset to transform.

Returns:

Transformed TSDataset.

Return type:

TSDataset