pmdarima.preprocessing.BoxCoxEndogTransformer

class pmdarima.preprocessing.BoxCoxEndogTransformer(lmbda=None, lmbda2=0, neg_action='raise', floor=1e-16)

Apply the Box-Cox transformation to an endogenous array

The Box-Cox transformation is applied to non-normal data to coerce it more towards a normal distribution. It’s specified as:

(((y + lam2) ** lam1) - 1) / lam1, if lmbda != 0, else
log(y + lam2)

Parameters:

lmbda : float or None, optional (default=None)

The lambda value for the Box-Cox transformation, if known. If not specified, it will be estimated via MLE.

lmbda2 : float, optional (default=0.)

The value to add to y to make it non-negative. If, after adding lmbda2, there are still negative values, a ValueError will be raised.

neg_action : str, optional (default=”raise”)

How to respond if any values in y <= 0 after adding lmbda2. One of (‘raise’, ‘warn’, ‘ignore’). If anything other than ‘raise’, values <= 0 will be truncated to the value of floor.

floor : float, optional (default=1e-16)

A positive value that truncate values to if there are values in y that are zero or negative and neg_action is not ‘raise’. Note that if values are truncated, invertibility will not be preserved, and the transformed array may not be perfectly inverse-transformed.

Methods

fit(y[, X])	Fit the transformer
fit_transform(y[, X])	Fit and transform the arrays
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
inverse_transform(y[, X])	Inverse transform a transformed array
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(y[, X])	Transform the new array

__init__(lmbda=None, lmbda2=0, neg_action='raise', floor=1e-16)

Examples using pmdarima.preprocessing.BoxCoxEndogTransformer

Displaying key timeseries statistics

Displaying key timeseries statistics