# FourierTransform¶

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

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 (Optional[int]) –

upper order of Fourier components to include;

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

• mods (Optional[Sequence[int]]) –

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 (Optional[str]) –

• 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

Inherited-members

Methods

 fit(ts) Fit the transform. fit_transform(ts) Fit and transform TSDataset. Return the list with regressors created by the transform. Inverse transform TSDataset. load(path) Load an object. 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)

Fit the transform.

Parameters

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

Returns

The fitted transform instance.

Return type

etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset)

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset

get_regressors_info() List[str][source]

Return the list with regressors created by the transform.

Return type

List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset)

Inverse transform TSDataset.

Do nothing.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – TSDataset to be inverse transformed.

Returns

TSDataset after applying inverse transformation.

Return type

etna.datasets.tsdataset.TSDataset

classmethod load(path: pathlib.Path) typing_extensions.Self

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 (pathlib.Path) – Path to load object from.

Returns

Return type

typing_extensions.Self

params_to_tune() [source]

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
save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) etna.core.mixins.TMixin

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 – Estimator parameters

• self (etna.core.mixins.TMixin) –

• params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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: etna.datasets.tsdataset.TSDataset)

Transform TSDataset inplace.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset to transform.

Returns

Transformed TSDataset.

Return type

etna.datasets.tsdataset.TSDataset