> ## 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.
# Scalers
# module `coreforecast.scalers`
## **Global Variables**
* **TYPE\_CHECKING**
***
## function `boxcox_lambda`
```python theme={null}
boxcox_lambda(
x: ndarray,
method: str,
season_length: Optional[int] = None,
lower: float = -0.9,
upper: float = 2.0
) → float
```
Find optimum lambda for the Box-Cox transformation
**Args:**
* `x` (np.ndarray): Array with data to transform.
* `method` (str): Method to use. Valid options are 'guerrero' and 'loglik'. 'guerrero' minimizes the coefficient of variation for subseries of `x` and supports negative values. 'loglik' maximizes the log-likelihood function.
* `season_length` (int, optional): Length of the seasonal period. Only required if method='guerrero'.
* `lower` (float): Lower bound for the lambda.
* `upper` (float): Upper bound for the lambda.
**Returns:**
* `float`: Optimum lambda.
***
## function `boxcox`
```python theme={null}
boxcox(x: ndarray, lmbda: float) → ndarray
```
Apply the Box-Cox transformation
**Args:**
* `x` (np.ndarray): Array with data to transform.
* `lmbda` (float): Lambda value to use.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
## function `inv_boxcox`
```python theme={null}
inv_boxcox(x: ndarray, lmbda: float) → ndarray
```
Invert the Box-Cox transformation
**Args:**
* `x` (np.ndarray): Array with data to transform.
* `lmbda` (float): Lambda value to use.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
## class `LocalMinMaxScaler`
Scale each group to the \[0, 1] interval
***
### method `fit`
```python theme={null}
fit(ga: 'GroupedArrayT') → _BaseLocalScaler
```
Compute the statistics for each group.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `self`: The fitted scaler object.
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
"Compute the statistics for each group and apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed statistics to invert the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(scalers: Sequence[ForwardRef('_BaseLocalScaler')]) → _BaseLocalScaler
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _BaseLocalScaler
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed statistics to apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
## class `LocalStandardScaler`
Scale each group to have zero mean and unit variance
***
### method `fit`
```python theme={null}
fit(ga: 'GroupedArrayT') → _BaseLocalScaler
```
Compute the statistics for each group.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `self`: The fitted scaler object.
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
"Compute the statistics for each group and apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed statistics to invert the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(scalers: Sequence[ForwardRef('_BaseLocalScaler')]) → _BaseLocalScaler
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _BaseLocalScaler
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed statistics to apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
## class `LocalRobustScaler`
Scale each group using robust statistics
**Args:**
* `scale` (str): Type of robust scaling to use. Valid options are 'iqr' and 'mad'. If 'iqr' will use the inter quartile range as the scale. If 'mad' will use median absolute deviation as the scale.
### method `__init__`
```python theme={null}
__init__(scale: str)
```
***
### method `fit`
```python theme={null}
fit(ga: 'GroupedArrayT') → _BaseLocalScaler
```
Compute the statistics for each group.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `self`: The fitted scaler object.
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
"Compute the statistics for each group and apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed statistics to invert the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(scalers: Sequence[ForwardRef('_BaseLocalScaler')]) → _BaseLocalScaler
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _BaseLocalScaler
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed statistics to apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
## class `LocalBoxCoxScaler`
Find the optimum lambda for the Box-Cox transformation by group and apply it
**Args:**
* `season_length` (int, optional): Length of the seasonal period. Only required if method='guerrero'.
* `lower` (float): Lower bound for the lambda.
* `upper` (float): Upper bound for the lambda.
* `method` (str): Method to use. Valid options are 'guerrero' and 'loglik'. 'guerrero' minimizes the coefficient of variation for subseries of `x` and supports negative values. 'loglik' maximizes the log-likelihood function.
### method `__init__`
```python theme={null}
__init__(
method: str,
season_length: Optional[int] = None,
lower: float = -0.9,
upper: float = 2.0
)
```
***
### method `fit`
```python theme={null}
fit(ga: 'GroupedArrayT') → _BaseLocalScaler
```
Compute the statistics for each group.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `self`: The fitted scaler object.
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
"Compute the statistics for each group and apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed lambdas to invert the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(scalers: Sequence[ForwardRef('_BaseLocalScaler')]) → _BaseLocalScaler
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → _BaseLocalScaler
```
***
### method `transform`
```python theme={null}
transform(ga: 'GroupedArrayT') → ndarray
```
Use the computed lambdas to apply the transformation.
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
## class `Difference`
Subtract a lag to each group
**Args:**
* `d` (int): Lag to subtract.
### method `__init__`
```python theme={null}
__init__(d: int)
```
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
Apply the transformation
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Invert the transformation
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(scalers: Sequence[ForwardRef('Difference')]) → Difference
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → Difference
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArrayT') → ndarray
```
Update the last observations from each serie
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the updated data.
***
## class `AutoDifferences`
Find and apply the optimal number of differences to each group.
**Args:**
* `max_diffs` (int): Maximum number of differences to apply.
### method `__init__`
```python theme={null}
__init__(max_diffs: int)
```
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
Compute and apply the optimal number of differences for each group
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Invert the differences
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(scalers: Sequence[ForwardRef('AutoDifferences')]) → AutoDifferences
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → AutoDifferences
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArrayT') → ndarray
```
Update the last observations from each serie
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the updated data.
***
## class `AutoSeasonalDifferences`
Find and apply the optimal number of seasonal differences to each group.
**Args:**
* `season_length` (int): Length of the seasonal period.
* `max_diffs` (int): Maximum number of differences to apply.
* `n_seasons` (int | None): Number of seasons to use to determine the number of differences. Defaults to 10. If `None` will use all samples, otherwise `season_length` \* `n_seasons` samples will be used for the test. Smaller values will be faster but could be less accurate.
### method `__init__`
```python theme={null}
__init__(season_length: int, max_diffs: int, n_seasons: Optional[int] = 10)
```
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
Compute and apply the optimal number of seasonal differences for each group
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Invert the seasonal differences
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(scalers: Sequence[ForwardRef('AutoDifferences')]) → AutoDifferences
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → AutoDifferences
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArrayT') → ndarray
```
Update the last observations from each serie
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the updated data.
***
## class `AutoSeasonalityAndDifferences`
Find the length of the seasonal period and apply the optimal number of differences to each group.
**Args:**
* `max_season_length` (int): Maximum length of the seasonal period.
* `max_diffs` (int): Maximum number of differences to apply.
* `n_seasons` (int | None): Number of seasons to use to determine the number of differences. Defaults to 10. If `None` will use all samples, otherwise `max_season_length` \* `n_seasons` samples will be used for the test. Smaller values will be faster but could be less accurate.
### method `__init__`
```python theme={null}
__init__(max_season_length: int, max_diffs: int, n_seasons: Optional[int] = 10)
```
***
### method `fit_transform`
```python theme={null}
fit_transform(ga: 'GroupedArrayT') → ndarray
```
Compute the optimal length of the seasonal period and apply the optimal number of differences for each group
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the transformed data.
***
### method `inverse_transform`
```python theme={null}
inverse_transform(ga: 'GroupedArrayT') → ndarray
```
Invert the seasonal differences
**Args:**
* `ga` (GroupedArray): Array with grouped data.
**Returns:**
* `np.ndarray`: Array with the inverted transformation.
***
### method `stack`
```python theme={null}
stack(
scalers: Sequence[ForwardRef('AutoSeasonalityAndDifferences')]
) → AutoSeasonalityAndDifferences
```
***
### method `take`
```python theme={null}
take(idxs: ndarray) → AutoSeasonalityAndDifferences
```
***
### method `update`
```python theme={null}
update(ga: 'GroupedArrayT') → ndarray
```
***
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