​
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

This file was automatically generated via lazydocs.

SeasonalExpanding