# 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`(ts) Inverse transform TSDataset. `load`(path) Load an object. `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.
get_regressors_info() List[str][source]

Return the list with regressors created by the transform.

Return type

List[str]