Lag transforms

Built-in lag transformations

RollingQuantile

RollingQuantile(p, window_size, min_samples=None, global_=False, groupby=None, **kwargs)

Bases: _RollingBase

Rolling statistic

RollingMax

Bases: _RollingBase

Rolling statistic

RollingMin

Bases: _RollingBase

Rolling statistic

RollingStd

Bases: _RollingBase

Rolling statistic

RollingMean

Bases: _RollingBase

Rolling statistic

SeasonalRollingQuantile

SeasonalRollingQuantile(p, season_length, window_size, min_samples=None, global_=False, groupby=None, **kwargs)

Bases: _Seasonal_RollingBase

Rolling statistic over seasonal periods

SeasonalRollingMax

Bases: _Seasonal_RollingBase

Rolling statistic over seasonal periods

SeasonalRollingMin

Bases: _Seasonal_RollingBase

Rolling statistic over seasonal periods

SeasonalRollingStd

Bases: _Seasonal_RollingBase

Rolling statistic over seasonal periods

SeasonalRollingMean

Bases: _Seasonal_RollingBase

Rolling statistic over seasonal periods

ExpandingQuantile

ExpandingQuantile(p, global_=False, groupby=None, **kwargs)

Bases: _ExpandingBase

Expanding statistic

Parameters:

Name	Type	Description	Default
global_	bool	If True, compute the statistic across all series aggregated by timestamp. Requires all series to end at the same timestamp. Defaults to False.	required
groupby	Sequence[str]	Column names to group by before computing the statistic. Columns must be static features. Mutually exclusive with global_. Defaults to None.	required

ExpandingMax

Bases: _ExpandingBase

Expanding statistic

Parameters:

Name	Type	Description	Default
global_	bool	If True, compute the statistic across all series aggregated by timestamp. Requires all series to end at the same timestamp. Defaults to False.	required
groupby	Sequence[str]	Column names to group by before computing the statistic. Columns must be static features. Mutually exclusive with global_. Defaults to None.	required

ExpandingMin

Bases: _ExpandingBase

Expanding statistic

Parameters:

Name	Type	Description	Default
global_	bool	If True, compute the statistic across all series aggregated by timestamp. Requires all series to end at the same timestamp. Defaults to False.	required
groupby	Sequence[str]	Column names to group by before computing the statistic. Columns must be static features. Mutually exclusive with global_. Defaults to None.	required

ExpandingStd

Bases: _ExpandingBase

Expanding statistic

Parameters:

Name	Type	Description	Default
global_	bool	If True, compute the statistic across all series aggregated by timestamp. Requires all series to end at the same timestamp. Defaults to False.	required
groupby	Sequence[str]	Column names to group by before computing the statistic. Columns must be static features. Mutually exclusive with global_. Defaults to None.	required

ExpandingMean

Bases: _ExpandingBase

Expanding statistic

Parameters:

Name	Type	Description	Default
global_	bool	If True, compute the statistic across all series aggregated by timestamp. Requires all series to end at the same timestamp. Defaults to False.	required
groupby	Sequence[str]	Column names to group by before computing the statistic. Columns must be static features. Mutually exclusive with global_. Defaults to None.	required

ExponentiallyWeightedMean

ExponentiallyWeightedMean(alpha, global_=False, groupby=None, **kwargs)

Bases: _BaseLagTransform

Exponentially weighted average

Parameters:

Name	Type	Description	Default
alpha	float	Smoothing factor.	required
global_	bool	If True, compute the statistic across all series aggregated by timestamp. Requires all series to end at the same timestamp. Defaults to False.	False
groupby	Sequence[str]	Column names to group by before computing the statistic. Columns must be static features. Mutually exclusive with global_. Defaults to None.	None

Offset

Offset(tfm, n)

Bases: _BaseLagTransform

Shift series before computing transformation

Parameters:

Name	Type	Description	Default
tfm	LagTransform	Transformation to be applied	required
n	int	Number of positions to shift (lag) series before applying the transformation	required

Combine

Combine(tfm1, tfm2, operator)

Bases: _BaseLagTransform

Combine two lag transformations using an operator

Parameters:

Name	Type	Description	Default
tfm1	LagTransform	First transformation.	required
tfm2	LagTransform	Second transformation.	required
operator	callable	Binary operator that defines how to combine the two transformations.	required