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module coreforecast.lag_transforms

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Global Variables

• TYPE_CHECKING

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class Lag

Simple lag operator

Args:

• lag (int): Number of periods to offset

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method __init__

__init__(lag: int)

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method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

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method take

take(_idxs: ndarray) → _BaseLagTransform

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method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class RollingMean

Rolling Mean

Args:

• lag (int): Number of periods to offset by before applying the transformation.
• window_size (int): Length of the rolling window.
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

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method __init__

__init__(lag: int, window_size: int, min_samples: Optional[int] = None)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

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class RollingStd

Rolling Standard Deviation

Args:

• lag (int): Number of periods to offset by before applying the transformation.
• window_size (int): Length of the rolling window.
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(lag: int, window_size: int, min_samples: Optional[int] = None)

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method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

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class RollingMin

Rolling Minimum

Args:

• lag (int): Number of periods to offset by before applying the transformation.
• window_size (int): Length of the rolling window.
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(lag: int, window_size: int, min_samples: Optional[int] = None)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class RollingMax

Rolling Maximum

Args:

• lag (int): Number of periods to offset by before applying the transformation.
• window_size (int): Length of the rolling window.
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(lag: int, window_size: int, min_samples: Optional[int] = None)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class RollingQuantile

Rolling quantile

Args:

• lag (int): Number of periods to offset by before applying the transformation
• p (float): Quantile to compute
• window_size (int): Length of the rolling window
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(
    lag: int,
    p: float,
    window_size: int,
    min_samples: Optional[int] = None
)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class SeasonalRollingMean

Seasonal rolling Mean

Args:

• lag (int): Number of periods to offset by before applying the transformation
• season_length (int): Length of the seasonal period, e.g. 7 for weekly data
• window_size (int): Length of the rolling window
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(
    lag: int,
    season_length: int,
    window_size: int,
    min_samples: Optional[int] = None
)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class SeasonalRollingStd

Seasonal rolling Standard Deviation

Args:

• lag (int): Number of periods to offset by before applying the transformation
• season_length (int): Length of the seasonal period, e.g. 7 for weekly data
• window_size (int): Length of the rolling window
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(
    lag: int,
    season_length: int,
    window_size: int,
    min_samples: Optional[int] = None
)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class SeasonalRollingMin

Seasonal rolling Minimum

Args:

• lag (int): Number of periods to offset by before applying the transformation
• season_length (int): Length of the seasonal period, e.g. 7 for weekly data
• window_size (int): Length of the rolling window
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(
    lag: int,
    season_length: int,
    window_size: int,
    min_samples: Optional[int] = None
)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class SeasonalRollingMax

Seasonal rolling Maximum

Args:

• lag (int): Number of periods to offset by before applying the transformation
• season_length (int): Length of the seasonal period, e.g. 7 for weekly data
• window_size (int): Length of the rolling window
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(
    lag: int,
    season_length: int,
    window_size: int,
    min_samples: Optional[int] = None
)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class SeasonalRollingQuantile

Seasonal rolling statistic

Args:

• lag (int): Number of periods to offset by before applying the transformation
• p (float): Quantile to compute
• season_length (int): Length of the seasonal period, e.g. 7 for weekly data
• window_size (int): Length of the rolling window
• min_samples (int, optional): Minimum number of samples required to compute the statistic. If None, defaults to window_size.

​

method __init__

__init__(
    lag: int,
    p: float,
    season_length: int,
    window_size: int,
    min_samples: Optional[int] = None
)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class ExpandingMean

Expanding Mean

Args:

• lag (int): Number of periods to offset by before applying the transformation

​

method __init__

__init__(lag: int)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(idxs: ndarray) → _ExpandingBase

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class ExpandingStd

Expanding Standard Deviation

Args:

• lag (int): Number of periods to offset by before applying the transformation

​

method __init__

__init__(lag: int)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(idxs: ndarray) → _ExpandingBase

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class ExpandingMin

Expanding Minimum

Args:

• lag (int): Number of periods to offset by before applying the transformation

​

method __init__

__init__(lag: int)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(idxs: ndarray) → _ExpandingBase

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class ExpandingMax

Expanding Maximum

Args:

• lag (int): Number of periods to offset by before applying the transformation

​

method __init__

__init__(lag: int)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(idxs: ndarray) → _ExpandingBase

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class ExpandingQuantile

Expanding quantile

Args: lag (int): Number of periods to offset by before applying the transformation p (float): Quantile to compute

​

method __init__

__init__(lag: int, p: float)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(_idxs: ndarray) → _BaseLagTransform

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

​

class ExponentiallyWeightedMean

Exponentially weighted mean

Args:

• lag (int): Number of periods to offset by before applying the transformation
• alpha (float): Smoothing factor

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method __init__

__init__(lag: int, alpha: float)

​

method stack

stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform

​

method take

take(idxs: ndarray) → ExponentiallyWeightedMean

​

method transform

transform(ga: 'GroupedArray') → ndarray

​

method update

update(ga: 'GroupedArray') → ndarray

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