TrendTransform

class TrendTransform(in_column: str, change_points_model: Optional[etna.transforms.decomposition.change_points_based.change_points_models.base.BaseChangePointsModelAdapter] = None, per_interval_model: Optional[etna.transforms.decomposition.change_points_based.per_interval_models.base.PerIntervalModel] = None, out_column: Optional[str] = None)

Bases: etna.transforms.decomposition.change_points_based.base.IrreversibleChangePointsTransform

Transform that adds trend as a feature.

Transform divides each segment into intervals using change_points_model. Then a separate model is fitted on each interval using per_interval_model. New column is created with values predicted by the model of each interval.

Evaluated function can be linear, mean, median, etc. Look at the signature to find out which models can be used.

Warning

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

Init TrendTransform.

Parameters

• in_column (str) – name of column to apply transform to

• change_points_model (Optional[etna.transforms.decomposition.change_points_based.change_points_models.base.BaseChangePointsModelAdapter]) – model to get trend change points, by default ruptures.detection.Binseg in a wrapper with n_bkps=5 is used

• per_interval_model (Optional[etna.transforms.decomposition.change_points_based.per_interval_models.base.PerIntervalModel]) – model to process intervals of segment, by default sklearn.linear_models.LinearRegression in a wrapper is used

• out_column (Optional[str]) – name of added column. If not given, use self.__repr__()

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.

Attributes

out_column

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

Get default grid for tuning hyperparameters.

If self.change_points_model is equal to default then this grid tunes parameters: change_points_model.change_points_model.model, change_points_model.n_bkps. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

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