BoxCoxTransformer

Box-Cox power transform with fittable lambda parameter.

Quickstart

from sktime.transformations.boxcox import BoxCoxTransformer

estimator = BoxCoxTransformer(bounds=None, method='mle', sp=None, lambda_fixed=0.0, enforce_positive=True)

Parameters(5)

bounds2-tuple of finite float

Initial bracket (lower, upper) for the optimization range when fitting the value of lambda. Default = unbounded. Ignored if method == "fixed". For half-open bounds pass a large bound value, e.g., (0, 1e12) for positive lambda. Infinity and nan as bound values are not supported.

method{“pearsonr”, “mle”, “guerrero”, “fixed”}, default=”mle”

The optimization approach used to determine the lambda value used in the Box-Cox transformation.

spint, optional, must be provided (only) if method=”guerrero”

Seasonal periodicity of the data in integer form. Only used if method=”guerrero” is chosen. Must be an integer >= 2.

lambda_fixedfloat, optional, default = 0.0

must be provided (only) if method=”fixed” default means that BoxCoxTransformer behaves like logarithm

enforce_positivebool, optional, default = True

If True`, in ``fit negative entries of X are replaced by their absolute values. In transform, the transform is applied to the absolute value while the sign is kept. If False, any negative values will be passed unchanged to the underlying functions (possibly causing error).

Examples

>>> from sktime.transformations.boxcox import BoxCoxTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = BoxCoxTransformer ()
>>> y_hat = transformer. fit_transform (y)

References

1. [1 ] Box, G. E. P. & Cox, D. R. (1964) An analysis of transformations, Journal of the Royal Statistical Society, Series B, 26, 211-252. [2 ] V.M. Guerrero, “Time-series analysis supported by Power Transformations “, Journal of Forecasting, vol. 12, pp. 37-48, 1993.