- class TimeSeriesImputerTransform(in_column: str = 'target', strategy: str = ImputerMode.zero, window: int = - 1, seasonality: int = 1, default_value: Optional[float] = None)¶
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.
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.
in_column (str) – name of processed column
strategy (str) –
filling value in missing timestamps:
If “zero”, then replace missing dates with zeros
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
window (int) –
In case of moving average and seasonality.
window=-1all 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
ValueError: – if incorrect strategy given
Fit transform on each segment.
May be reimplemented.
Apply inverse_transform to each segment.
Apply transform to each segment separately.