Rolling - Nixtla

`rolling_mean`

rolling_mean(x, window_size, min_samples=None, skipna=False)

Compute the rolling_mean of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the rolling statistic

`rolling_std`

rolling_std(x, window_size, min_samples=None, skipna=False)

Compute the rolling_std of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the rolling statistic

`rolling_min`

rolling_min(x, window_size, min_samples=None, skipna=False)

Compute the rolling_min of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the rolling statistic

`rolling_max`

rolling_max(x, window_size, min_samples=None, skipna=False)

Compute the rolling_max of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the rolling statistic

`rolling_quantile`

rolling_quantile(x, p, window_size, min_samples=None, skipna=False)

Compute the rolling_quantile of the input array. Parameters:

Name	Type	Description	Default
`x`	`ndarray`	Input array.	required
`p`	`float`	Quantile to compute.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	`None`
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	`False`

Returns:

Type	Description
`ndarray`	np.ndarray: Array with rolling statistic

`seasonal_rolling_mean`

seasonal_rolling_mean(x, season_length, window_size, min_samples=None, skipna=False)

Compute the seasonal_rolling_mean of the input array Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`season_length`	`int`	The length of the seasonal period.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the seasonal rolling statistic

`seasonal_rolling_std`

seasonal_rolling_std(x, season_length, window_size, min_samples=None, skipna=False)

Compute the seasonal_rolling_std of the input array Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`season_length`	`int`	The length of the seasonal period.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the seasonal rolling statistic

`seasonal_rolling_min`

seasonal_rolling_min(x, season_length, window_size, min_samples=None, skipna=False)

Compute the seasonal_rolling_min of the input array Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`season_length`	`int`	The length of the seasonal period.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the seasonal rolling statistic

`seasonal_rolling_max`

seasonal_rolling_max(x, season_length, window_size, min_samples=None, skipna=False)

Compute the seasonal_rolling_max of the input array Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`season_length`	`int`	The length of the seasonal period.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	required
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	required

Returns:

Type	Description
np.ndarray: Array with the seasonal rolling statistic

`seasonal_rolling_quantile`

seasonal_rolling_quantile(x, p, season_length, window_size, min_samples=None, skipna=False)

Compute the seasonal_rolling_quantile of the input array. Parameters:

Name	Type	Description	Default
`x`	`ndarray`	Input array.	required
`p`	`float`	Quantile to compute.	required
`season_length`	`int`	The length of the seasonal period.	required
`window_size`	`int`	The size of the rolling window.	required
`min_samples`	`int`	The minimum number of samples required to compute the statistic. If None, it is set to `window_size`.	`None`
`skipna`	`bool`	Exclude NaN values from calculations. When False (default), any NaN value in the window causes the result to be NaN. When True, NaN values are ignored and statistics are computed on remaining valid values in the window. Defaults to False for backwards compatibility.	`False`

Returns:

Type	Description
`ndarray`	np.ndarray: Array with rolling statistic

​

​rolling_mean

​rolling_std

​rolling_min

​rolling_max

​rolling_quantile

​seasonal_rolling_mean

​seasonal_rolling_std

​seasonal_rolling_min

​seasonal_rolling_max

​seasonal_rolling_quantile

`rolling_mean`

`rolling_std`

`rolling_min`

`rolling_max`

`rolling_quantile`

`seasonal_rolling_mean`

`seasonal_rolling_std`

`seasonal_rolling_min`

`seasonal_rolling_max`

`seasonal_rolling_quantile`