general

General feature transforms for time series data.

This module provides general-purpose transforms for time series datasets, including data cleaning, normalization, and feature engineering utilities that can be applied across various domains.

Submodules

openstef_models.transforms.general.dimensionality_reducer	Transform for dimensionality reduction in time series data.
openstef_models.transforms.general.empty_feature_remover	Transform for removing completely empty columns from time series datasets.
openstef_models.transforms.general.flagger	Transform for flagging feature values inside or outside observed training ranges.
openstef_models.transforms.general.imputer	Missing values imputation transform for time series datasets.
openstef_models.transforms.general.nan_dropper	Transform for dropping rows containing NaN values.
openstef_models.transforms.general.outlier_handler	Transform for handling out-of-range feature values.
openstef_models.transforms.general.sample_weighter	Sample weighting for time series forecasting models.
openstef_models.transforms.general.scaler	Transform for scaling features in time series data.
openstef_models.transforms.general.selector	Transform for dropping for dropping features from dataset based on FeatureSelection.
openstef_models.transforms.general.shifter	Transform for shifting features to align aggregation intervals.

Classes

DimensionalityReducer(**data)	Reduce the dimensionality of a given set of features.
EmptyFeatureRemover(**data)	Transform that removes columns which are completely empty (all values are missing).
Flagger(**data)	Transform that flags specified features to their observed min and max values.
Imputer(**data)	Transform that imputes missing values in specified columns of time series data.
NaNDropper(**data)	Transform that drops rows containing NaN values in selected columns.
OutlierHandler(**data)	Transform that handles out-of-range values for selected features.
SampleWeightConfig(**data)	Configuration for sample weighting parameters.
SampleWeighter(**data)	Transform that adds sample weights based on target variable distribution.
Scaler(**data)	Transform that scales time series data using various scikit-learn scaling methods.
Selector(**data)	Selects features based on FeatureSelection.
Shifter(**data)	Transform that shifts features to align their aggregation interval with the target.