> ## 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.
# Rolling
# module `coreforecast.rolling`
***
## function `rolling_mean`
```python theme={null}
rolling_mean(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the rolling\_mean of the input array.
**Args:**
* `x` (np.ndarray): Input array.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the rolling statistic
***
## function `rolling_std`
```python theme={null}
rolling_std(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the rolling\_std of the input array.
**Args:**
* `x` (np.ndarray): Input array.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the rolling statistic
***
## function `rolling_min`
```python theme={null}
rolling_min(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the rolling\_min of the input array.
**Args:**
* `x` (np.ndarray): Input array.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the rolling statistic
***
## function `rolling_max`
```python theme={null}
rolling_max(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the rolling\_max of the input array.
**Args:**
* `x` (np.ndarray): Input array.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the rolling statistic
***
## function `rolling_quantile`
```python theme={null}
rolling_quantile(
x: ndarray,
p: float,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the rolling\_quantile of the input array.
**Args:**
* `x` (np.ndarray): Input array.
* `q` (float): Quantile to compute.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with rolling statistic
***
## function `seasonal_rolling_mean`
```python theme={null}
seasonal_rolling_mean(
x: ndarray,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the seasonal\_rolling\_mean of the input array
**Args:**
* `x` (np.ndarray): Input array.
* `season_length` (int): The length of the seasonal period.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the seasonal rolling statistic
***
## function `seasonal_rolling_std`
```python theme={null}
seasonal_rolling_std(
x: ndarray,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the seasonal\_rolling\_std of the input array
**Args:**
* `x` (np.ndarray): Input array.
* `season_length` (int): The length of the seasonal period.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the seasonal rolling statistic
***
## function `seasonal_rolling_min`
```python theme={null}
seasonal_rolling_min(
x: ndarray,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the seasonal\_rolling\_min of the input array
**Args:**
* `x` (np.ndarray): Input array.
* `season_length` (int): The length of the seasonal period.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the seasonal rolling statistic
***
## function `seasonal_rolling_max`
```python theme={null}
seasonal_rolling_max(
x: ndarray,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the seasonal\_rolling\_max of the input array
**Args:**
* `x` (np.ndarray): Input array.
* `season_length` (int): The length of the seasonal period.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with the seasonal rolling statistic
***
## function `seasonal_rolling_quantile`
```python theme={null}
seasonal_rolling_quantile(
x: ndarray,
p: float,
season_length: int,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```
Compute the seasonal\_rolling\_quantile of the input array.
**Args:**
* `x` (np.ndarray): Input array.
* `q` (float): Quantile to compute.
* `season_length` (int): The length of the seasonal period.
* `window_size` (int): The size of the rolling window.
* `min_samples` (int, optional): The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.
**Returns:**
* `np.ndarray`: Array with rolling statistic
***
*This file was automatically generated via [lazydocs](https://github.com/ml-tooling/lazydocs).*