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:

• 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

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

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

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

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

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

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

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

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

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

---

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