`module` `mlforecast.lag_transforms`

`class` `Lag`

`method` `init`

__init__(lag: int)

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `RollingMean`

`method` `init`

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

Args:

window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `RollingStd`

`method` `init`

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

Args:

window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `RollingMin`

`method` `init`

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

Args:

window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `RollingMax`

`method` `init`

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

Args:

window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `RollingQuantile`

`method` `init`

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

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `SeasonalRollingMean`

`method` `init`

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

Args:

season_length (int): Periodicity of the seasonal period.
window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `SeasonalRollingStd`

`method` `init`

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

Args:

season_length (int): Periodicity of the seasonal period.
window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `SeasonalRollingMin`

`method` `init`

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

Args:

season_length (int): Periodicity of the seasonal period.
window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `SeasonalRollingMax`

`method` `init`

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

Args:

season_length (int): Periodicity of the seasonal period.
window_size (int): Number of samples in the window.
min_samples (int, optional): Minimum samples required to output the statistic. If None, will be set to window_size. Defaults to None.

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `SeasonalRollingQuantile`

`method` `init`

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

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `ExpandingMean`

`method` `init`

__init__()

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `ExpandingStd`

`method` `init`

__init__()

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `ExpandingMin`

`method` `init`

__init__()

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `ExpandingMax`

`method` `init`

__init__()

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `ExpandingQuantile`

`method` `init`

__init__(p: float)

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `ExponentiallyWeightedMean`

Exponentially weighted average Args:

alpha (float): Smoothing factor.

`method` `init`

__init__(alpha: float)

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `Offset`

Shift series before computing transformation Args:

tfm (LagTransform): Transformation to be applied
n (int): Number of positions to shift (lag) series before applying the transformation

`method` `init`

__init__(tfm: _BaseLagTransform, n: int)

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`class` `Combine`

Combine two lag transformations using an operator Args:

tfm1 (LagTransform): First transformation.
tfm2 (LagTransform): Second transformation.
operator (callable): Binary operator that defines how to combine the two transformations.

`method` `init`

__init__(tfm1: _BaseLagTransform, tfm2: _BaseLagTransform, operator: Callable)

`property` update_samples

`method` `stack`

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

`method` `take`

take(idxs: ndarray) → _BaseLagTransform

`method` `transform`

transform(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

`method` `update`

update(
    ga: <built-in method GroupedArray of PyCapsule object at 0x7f8674124db0>
) → ndarray

Getting Started

How-to guides

Tutorials

API Reference

​module mlforecast.lag_transforms

​class Lag

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class RollingMean

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class RollingStd

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class RollingMin

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class RollingMax

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class RollingQuantile

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class SeasonalRollingMean

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class SeasonalRollingStd

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class SeasonalRollingMin

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class SeasonalRollingMax

​method __init__

​property update_samples

​method stack

​method take

​method transform

​method update

​class SeasonalRollingQuantile

​method __init__

​property update_samples

​method stack

​method take

`module` `mlforecast.lag_transforms`

`class` `Lag`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `RollingMean`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `RollingStd`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `RollingMin`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `RollingMax`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `RollingQuantile`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `SeasonalRollingMean`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `SeasonalRollingStd`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `SeasonalRollingMin`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `SeasonalRollingMax`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `SeasonalRollingQuantile`

`method` `init`

`property` update_samples

`method` `stack`

`method` `take`

`method` `transform`

`method` `update`

`class` `ExpandingMean`

`method` `init`