LinearTrendTransform

class LinearTrendTransform(in_column: str, poly_degree: int = 1, **regression_params)

Bases: etna.transforms.base.ReversiblePerSegmentWrapper

Transform that uses linear regression with polynomial features to make a detrending.

Transform fits a sklearn.linear_model.LinearRegression with polynomial features on each segment. Values predicted by the model are subtracted from each segment.

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Create instance of LinearTrendTransform.

Parameters

• in_column (str) – name of processed column

• poly_degree (int) – degree of polynomial to fit trend on

• regression_params – params that should be used to init sklearn.linear_model.LinearRegression

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.

This grid tunes only poly_degree parameter. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]