TimeSeriesImputerTransform

class TimeSeriesImputerTransform(in_column: str = 'target', strategy: str = ImputerMode.constant, window: int = - 1, seasonality: int = 1, default_value: Optional[float] = None, constant_value: float = 0)

Bases: etna.transforms.base.ReversibleTransform

Transform to fill NaNs in series of a given dataframe.

• It is assumed that given series begins with first non NaN value.

• This transform can’t fill NaNs in the future, only on train data.

• This transform can’t fill NaNs if all values are NaNs. In this case exception is raised.

Warning

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

Create instance of TimeSeriesImputerTransform.

Parameters

• in_column (str) – name of processed column

• strategy (str) –

  filling value in missing timestamps:

  • If “mean”, then replace missing dates using the mean in fit stage.

  • If “running_mean” then replace missing dates using mean of subset of data

  • If “forward_fill” then replace missing dates using last existing value

  • If “seasonal” then replace missing dates using seasonal moving average

  • If “constant” then replace missing dates using constant value.

• window (int) –

  In case of moving average and seasonality.

  • If window=-1 all previous dates are taken in account

  • Otherwise only window previous dates

• seasonality (int) – the length of the seasonality

• default_value (Optional[float]) – value which will be used to impute the NaNs left after applying the imputer with the chosen strategy

• constant_value (float) – value to fill gaps in “constant” strategy

Raises

ValueError: – if incorrect strategy given

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 parameters: strategy, window. Other parameters are expected to be set by the user.

Strategy “seasonal” is suggested only if self.seasonality is set higher than 1.

Returns

Grid to tune.

Return type

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