TrendTransform

class TrendTransform(in_column: str, out_column: Optional[str] = None, detrend_model: Type[sklearn.base.RegressorMixin] = LinearRegression(), model: str = 'ar', custom_cost: Optional[ruptures.base.BaseCost] = None, min_size: int = 2, jump: int = 1, n_bkps: int = 5, pen: Optional[float] = None, epsilon: Optional[float] = None)

Bases: etna.transforms.decomposition.trend._TrendTransform, etna.transforms.base.FutureMixin

TrendTransform adds trend as a feature.

TrendTransform uses uses ruptures.detection.Binseg model as a change point detection model in _TrendTransform.

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

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

• detrend_model (Type[sklearn.base.RegressorMixin]) – model to get trend in data

• model (str) – binseg segment model, [“l1”, “l2”, “rbf”,…]. Not used if ‘custom_cost’ is not None.

• custom_cost (Optional[ruptures.base.BaseCost]) – binseg custom cost function

• min_size (int) – minimum segment length necessary to decide it is a stable trend segment

• jump (int) – jump value can speed up computations: if jump==k, the algo will use every k-th value for change points search.

• n_bkps (int) – number of change points to find

• pen (Optional[float]) – penalty value (>0)

• epsilon (Optional[float]) – reconstruction budget (>0)

Inherited-members

Methods

fit(df)	Fit transform on each segment.
fit_transform(df)	May be reimplemented.
inverse_transform(df)	Apply inverse_transform to each segment.
transform(df)	Apply transform to each segment separately.