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.

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