Transforms

Details and available algorithms

See the API documentation for further details on available feature extractions and transformations:

etna.transforms.math.add_constant.AddConstTransform(...)	AddConstTransform add constant for given series.
etna.transforms.decomposition.change_points_based.base.BaseChangePointsTransform()	Base class for all the change points based transforms.
etna.transforms.math.power.BoxCoxTransform([...])	BoxCoxTransform applies Box-Cox transformation to DataFrame.
etna.transforms.decomposition.change_points_based.level.ChangePointsLevelTransform(...)	Transform that makes a detrending of change-point intervals.
etna.transforms.decomposition.change_points_based.segmentation.ChangePointsSegmentationTransform(...)	Transform that makes label encoding of change-point intervals.
etna.transforms.decomposition.change_points_based.detrend.ChangePointsTrendTransform(...)	Transform that makes a detrending of change-point intervals.
etna.transforms.timestamp.date_flags.DateFlagsTransform([...])	DateFlagsTransform is a class that implements extraction of the main date-based features from datetime column.
etna.transforms.outliers.point_outliers.DensityOutliersTransform(...)	Transform that uses get_anomalies_density() to find anomalies in data.
etna.transforms.decomposition.deseasonal.DeseasonalityTransform(...)	Transform that uses statsmodels.tsa.seasonal.seasonal_decompose() to subtract seasonal component from the data.
etna.transforms.math.differencing.DifferencingTransform(...)	Calculate a time series differences.
etna.transforms.math.lags.ExogShiftTransform(lag)	Shifts exogenous variables from a given dataframe.
etna.transforms.feature_selection.filter.FilterFeaturesTransform([...])	Filters features in each segment of the dataframe.
etna.transforms.timestamp.fourier.FourierTransform(period)	Adds fourier features to the dataset.
etna.transforms.feature_selection.gale_shapley.GaleShapleyFeatureSelectionTransform(...)	Transform that provides feature filtering by Gale-Shapley matching algorithm according to the relevance table.
etna.transforms.timestamp.holiday.HolidayTransform([...])	HolidayTransform generates series that indicates holidays in given dataset.
etna.transforms.decomposition.change_points_based.base.IrreversibleChangePointsTransform(...)	IrreversibleChangePointsTransform class is a base class for all irreversible transforms that work with change point.
etna.transforms.base.IrreversiblePerSegmentWrapper(...)	Class to apply irreversible transform in per segment manner.
etna.transforms.base.IrreversibleTransform(...)	Base class to create irreversible transforms.
etna.transforms.encoders.categorical.LabelEncoderTransform(...)	Encode categorical feature with value between 0 and n_classes-1.
etna.transforms.math.lags.LagTransform(...)	Generates series of lags from given dataframe.
etna.transforms.math.apply_lambda.LambdaTransform(...)	LambdaTransform applies input function for given series.
etna.transforms.decomposition.detrend.LinearTrendTransform(...)	Transform that uses linear regression with polynomial features to make a detrending.
etna.transforms.math.log.LogTransform(in_column)	LogTransform applies logarithm transformation for given series.
etna.transforms.math.statistics.MADTransform(...)	MADTransform computes Mean Absolute Deviation over the window.
etna.transforms.feature_selection.feature_importance.MRMRFeatureSelectionTransform(...)	Transform that selects features according to MRMR variable selection method adapted to the timeseries case.
etna.transforms.math.scalers.MaxAbsScalerTransform([...])	Scale each feature by its maximum absolute value.
etna.transforms.math.statistics.MaxTransform(...)	MaxTransform computes max value for given window.
etna.transforms.encoders.mean_segment_encoder.MeanSegmentEncoderTransform()	Makes expanding mean target encoding of the segment.
etna.transforms.math.statistics.MeanTransform(...)	MeanTransform computes average value for given window.
etna.transforms.outliers.point_outliers.MedianOutliersTransform(...)	Transform that uses get_anomalies_median() to find anomalies in data.
etna.transforms.math.statistics.MedianTransform(...)	MedianTransform computes median value for given window.
etna.transforms.math.statistics.MinMaxDifferenceTransform(...)	MinMaxDifferenceTransform computes difference between max and min values for given window.
etna.transforms.math.scalers.MinMaxScalerTransform([...])	Transform features by scaling each feature to a given range.
etna.transforms.math.statistics.MinTransform(...)	MinTransform computes min value for given window.
etna.transforms.encoders.categorical.OneHotEncoderTransform(...)	Encode categorical feature as a one-hot numeric features.
etna.transforms.base.OneSegmentTransform()	Base class to create one segment transforms to apply to data.
etna.transforms.base.PerSegmentWrapper(...)	Class to apply transform in per segment manner.
etna.transforms.outliers.point_outliers.PredictionIntervalOutliersTransform(...)	Transform that uses get_anomalies_prediction_interval() to find anomalies in data.
etna.transforms.math.statistics.QuantileTransform(...)	QuantileTransform computes quantile value for given window.
etna.transforms.missing_values.resample.ResampleWithDistributionTransform(...)	ResampleWithDistributionTransform resamples the given column using the distribution of the other column.
etna.transforms.decomposition.change_points_based.base.ReversibleChangePointsTransform(...)	ReversibleChangePointsTransform class is a base class for all reversible transforms that work with change point.
etna.transforms.base.ReversiblePerSegmentWrapper(...)	Class to apply reversible transform in per segment manner.
etna.transforms.base.ReversibleTransform(...)	Base class to create reversible transforms.
etna.transforms.math.scalers.RobustScalerTransform([...])	Scale features using statistics that are robust to outliers.
etna.transforms.decomposition.stl.STLTransform(...)	Transform that uses statsmodels.tsa.seasonal.STL to subtract season and trend from the data.
etna.transforms.encoders.segment_encoder.SegmentEncoderTransform()	Encode segment label to categorical.
etna.transforms.timestamp.special_days.SpecialDaysTransform([...])	SpecialDaysTransform generates series that indicates is weekday/monthday is special in given dataframe.
etna.transforms.math.scalers.StandardScalerTransform([...])	Standardize features by removing the mean and scaling to unit variance.
etna.transforms.math.statistics.StdTransform(...)	StdTransform computes std value for given window.
etna.transforms.math.statistics.SumTransform(...)	SumTransform computes sum of values over given window.
etna.transforms.decomposition.detrend.TheilSenTrendTransform(...)	Transform that uses Theil–Sen regression with polynomial features to make a detrending.
etna.transforms.timestamp.time_flags.TimeFlagsTransform([...])	TimeFlagsTransform is a class that implements extraction of the main time-based features from datetime column.
etna.transforms.missing_values.imputation.TimeSeriesImputerTransform([...])	Transform to fill NaNs in series of a given dataframe.
etna.transforms.base.Transform(required_features)	Base class to create any transforms to apply to data.
etna.transforms.feature_selection.feature_importance.TreeFeatureSelectionTransform(...)	Transform that selects features according to tree-based models feature importance.
etna.transforms.decomposition.change_points_based.trend.TrendTransform(...)	Transform that adds trend as a feature.
etna.transforms.math.power.YeoJohnsonTransform([...])	YeoJohnsonTransform applies Yeo-Johns transformation to a DataFrame.