MeanTransform

class MeanTransform(in_column: str, window: int, seasonality: int = 1, alpha: float = 1, min_periods: int = 1, fillna: float = 0, out_column: Optional[str] = None)

Bases: etna.transforms.math.statistics.WindowStatisticsTransform

MeanTransform computes average value for given window.

\[MeanTransform(x_t) = \sum_{i=1}^{window}{x_{t - i}\cdot\alpha^{i - 1}}\]

Init MeanTransform.

Parameters

• in_column (str) – name of processed column

• window (int) – size of window to aggregate

• seasonality (int) – seasonality of lags to compute window’s aggregation with

• alpha (float) – autoregressive coefficient

• min_periods (int) – min number of targets in window to compute aggregation; if there is less than min_periods number of targets return None

• fillna (float) – value to fill results NaNs with

• out_column (str, optional) – result column name. 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.

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

Get default grid for tuning hyperparameters.

This grid tunes parameters: window, alpha. Other parameters are expected to be set by the user.

Returns

Grid to tune.

Return type

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