API Reference

# Rolling

`module` `coreforecast.rolling`

`function` `rolling_mean`

```
rolling_mean(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```

Compute the rolling_mean of the input array.

**Args:**

(np.ndarray): Input array.`x`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the rolling statistic`np.ndarray`

`function` `rolling_std`

```
rolling_std(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```

Compute the rolling_std of the input array.

**Args:**

(np.ndarray): Input array.`x`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the rolling statistic`np.ndarray`

`function` `rolling_min`

```
rolling_min(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```

Compute the rolling_min of the input array.

**Args:**

(np.ndarray): Input array.`x`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the rolling statistic`np.ndarray`

`function` `rolling_max`

```
rolling_max(
x: ndarray,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```

Compute the rolling_max of the input array.

**Args:**

(np.ndarray): Input array.`x`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the rolling statistic`np.ndarray`

`function` `rolling_quantile`

```
rolling_quantile(
x: ndarray,
p: float,
window_size: int,
min_samples: Optional[int] = None
) → ndarray
```

Compute the rolling_quantile of the input array.

**Args:**

(np.ndarray): Input array.`x`

(float): Quantile to compute.`q`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with rolling statistic`np.ndarray`

`function` `seasonal_rolling_mean`

```
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:**

(np.ndarray): Input array.`x`

(int): The length of the seasonal period.`season_length`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the seasonal rolling statistic`np.ndarray`

`function` `seasonal_rolling_std`

```
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:**

(np.ndarray): Input array.`x`

(int): The length of the seasonal period.`season_length`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the seasonal rolling statistic`np.ndarray`

`function` `seasonal_rolling_min`

```
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:**

(np.ndarray): Input array.`x`

(int): The length of the seasonal period.`season_length`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the seasonal rolling statistic`np.ndarray`

`function` `seasonal_rolling_max`

```
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:**

(np.ndarray): Input array.`x`

(int): The length of the seasonal period.`season_length`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with the seasonal rolling statistic`np.ndarray`

`function` `seasonal_rolling_quantile`

```
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:**

(np.ndarray): Input array.`x`

(float): Quantile to compute.`q`

(int): The length of the seasonal period.`season_length`

(int): The size of the rolling window.`window_size`

(int, optional): The minimum number of samples required to compute the statistic. If None, it is set to`min_samples`

`window_size`

.

**Returns:**

: Array with rolling statistic`np.ndarray`

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