coreforecast.scalers
boxcox_lambda
x
(np.ndarray): Array with data to transform.method
(str): Method to use. Valid options are ‘guerrero’ and ‘loglik’. ‘guerrero’ minimizes the coefficient of variation for subseries of x
and supports negative values. ‘loglik’ maximizes the log-likelihood function.season_length
(int, optional): Length of the seasonal period. Only required if method=‘guerrero’.lower
(float): Lower bound for the lambda.upper
(float): Upper bound for the lambda.float
: Optimum lambda.boxcox
x
(np.ndarray): Array with data to transform.lmbda
(float): Lambda value to use.np.ndarray
: Array with the transformed data.inv_boxcox
x
(np.ndarray): Array with data to transform.lmbda
(float): Lambda value to use.np.ndarray
: Array with the inverted transformation.LocalMinMaxScaler
fit
ga
(GroupedArray): Array with grouped data.self
: The fitted scaler object.fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.LocalStandardScaler
fit
ga
(GroupedArray): Array with grouped data.self
: The fitted scaler object.fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.LocalRobustScaler
scale
(str): Type of robust scaling to use. Valid options are ‘iqr’ and ‘mad’. If ‘iqr’ will use the inter quartile range as the scale. If ‘mad’ will use median absolute deviation as the scale.__init__
fit
ga
(GroupedArray): Array with grouped data.self
: The fitted scaler object.fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.LocalBoxCoxScaler
season_length
(int, optional): Length of the seasonal period. Only required if method=‘guerrero’.lower
(float): Lower bound for the lambda.upper
(float): Upper bound for the lambda.method
(str): Method to use. Valid options are ‘guerrero’ and ‘loglik’. ‘guerrero’ minimizes the coefficient of variation for subseries of x
and supports negative values. ‘loglik’ maximizes the log-likelihood function.__init__
fit
ga
(GroupedArray): Array with grouped data.self
: The fitted scaler object.fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.Difference
d
(int): Lag to subtract.__init__
fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
update
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the updated data.AutoDifferences
max_diffs
(int): Maximum number of differences to apply.__init__
fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
update
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the updated data.AutoSeasonalDifferences
season_length
(int): Length of the seasonal period.max_diffs
(int): Maximum number of differences to apply.n_seasons
(int | None): Number of seasons to use to determine the number of differences. Defaults to 10. If None
will use all samples, otherwise season_length
* n_seasons
samples will be used for the test. Smaller values will be faster but could be less accurate.__init__
fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
update
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the updated data.AutoSeasonalityAndDifferences
max_season_length
(int): Maximum length of the seasonal period.max_diffs
(int): Maximum number of differences to apply.n_seasons
(int | None): Number of seasons to use to determine the number of differences. Defaults to 10. If None
will use all samples, otherwise max_season_length
* n_seasons
samples will be used for the test. Smaller values will be faster but could be less accurate.__init__
fit_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the transformed data.inverse_transform
ga
(GroupedArray): Array with grouped data.np.ndarray
: Array with the inverted transformation.stack
take
update