etna.transforms.FourierTransform

class FourierTransform(period: float, order: int | None = None, mods: Sequence[int] | None = None, out_column: str | None = None)

Bases: IrreversibleTransform, FutureMixin

Adds fourier features to the dataset.

Notes

To understand how transform works we recommend: Fourier series.

• Parameter period is responsible for the seasonality we want to capture.

• Parameters order and mods define which harmonics will be used.

Parameter order is a more user-friendly version of mods. For example, order=2 can be represented as mods=[1, 2, 3, 4] if period > 4 and as mods=[1, 2, 3] if 3 <= period <= 4.

Create instance of FourierTransform.

Parameters:

• period (float) –

  the period of the seasonality to capture in frequency units of time series;

  period should be >= 2

• order (int | None) –

  upper order of Fourier components to include;

  order should be >= 1 and <= ceil(period/2))

• mods (Sequence[int] | None) –

  alternative and precise way of defining which harmonics will be used, for example mods=[1, 3, 4] means that sin of the first order and sin and cos of the second order will be used;

  mods should be >= 1 and < period

• out_column (str | None) –

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

  • if don’t set, name will be transform.__repr__(), repr will be made for transform that creates exactly this column

Raises:

• ValueError: – if period < 2

• ValueError: – if both or none of order, mods is set

• ValueError: – if order is < 1 or > ceil(period/2)

• ValueError: – if at least one mod is < 1 or >= period

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) → Transform

Fit the transform.

Parameters:

ts (TSDataset) – Dataset to fit the transform on.

Returns:

The fitted transform instance.

Return type:

Transform

fit_transform(ts: TSDataset) → TSDataset

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]

Return the list with regressors created by the transform.

Return type:

List[str]

inverse_transform(ts: TSDataset) → TSDataset

Inverse transform TSDataset.

Do nothing.

Parameters:

ts (TSDataset) – TSDataset to be inverse transformed.

Returns:

TSDataset after applying inverse transformation.

Return type:

TSDataset

classmethod load(path: Path) → Self

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]

Get default grid for tuning hyperparameters.

If self.order is set then 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)

Save the object.

Parameters:

path (Path) – Path to save object to.

set_params(**params: dict) → Self

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 = 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()

Collect all information about etna object in dict.

transform(ts: TSDataset) → TSDataset

Transform TSDataset inplace.

Parameters:

ts (TSDataset) – Dataset to transform.

Returns:

Transformed TSDataset.

Return type:

TSDataset