> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Lag transforms
# module `coreforecast.lag_transforms`
## **Global Variables**
* **TYPE\_CHECKING**
***
## class `Lag`
Simple lag operator
**Args:**
* `lag` (int): Number of periods to offset
### method `__init__`
```python theme={null}
__init__(lag: int)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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.
### method `__init__`
```python theme={null}
__init__(lag: int, window_size: int, min_samples: Optional[int] = None)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArray') → ndarray
```
***
## 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__`
```python theme={null}
__init__(lag: int, window_size: int, min_samples: Optional[int] = None)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArray') → ndarray
```
***
## 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__`
```python theme={null}
__init__(lag: int, window_size: int, min_samples: Optional[int] = None)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(lag: int, window_size: int, min_samples: Optional[int] = None)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(
lag: int,
p: float,
window_size: int,
min_samples: Optional[int] = None
)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(
lag: int,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(
lag: int,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(
lag: int,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(
lag: int,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(
lag: int,
p: float,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArray') → ndarray
```
***
## class `ExpandingMean`
Expanding Mean
**Args:**
* `lag` (int): Number of periods to offset by before applying the transformation
### method `__init__`
```python theme={null}
__init__(lag: int)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _ExpandingBase
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArray') → ndarray
```
***
## class `ExpandingStd`
Expanding Standard Deviation
**Args:**
* `lag` (int): Number of periods to offset by before applying the transformation
### method `__init__`
```python theme={null}
__init__(lag: int)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _ExpandingBase
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArray') → ndarray
```
***
## class `ExpandingMin`
Expanding Minimum
**Args:**
* `lag` (int): Number of periods to offset by before applying the transformation
### method `__init__`
```python theme={null}
__init__(lag: int)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _ExpandingBase
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArray') → ndarray
```
***
## class `ExpandingMax`
Expanding Maximum
**Args:**
* `lag` (int): Number of periods to offset by before applying the transformation
### method `__init__`
```python theme={null}
__init__(lag: int)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _ExpandingBase
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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__`
```python theme={null}
__init__(lag: int, p: float)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(_idxs: ndarray) → _BaseLagTransform
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
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
### method `__init__`
```python theme={null}
__init__(lag: int, alpha: float)
```
***
### method `stack`
```python theme={null}
stack(transforms: Sequence[ForwardRef('_BaseLagTransform')]) → _BaseLagTransform
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → ExponentiallyWeightedMean
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArray') → ndarray
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArray') → ndarray
```
***
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