FourierTransform

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

Bases: etna.transforms.base.IrreversibleTransform, etna.transforms.base.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 (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.
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.

get_regressors_info() → List[str]

Return the list with regressors created by the transform.

Return type

List[str]

params_to_tune() → Dict[str, etna.distributions.distributions.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, etna.distributions.distributions.BaseDistribution]