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ScaledAsinhTransformer

API referenceSource
MultivariateInverse transformUnequal length

Hyperbolic sine transformation and its inverse [1].

Quickstart

python
from sktime.transformations.scaledasinh import ScaledAsinhTransformer

estimator = ScaledAsinhTransformer(mad_normalization_factor=1.4826)

Parameters(1)

mad_normalization_factorfloat, default = 1.4826

The normalization factor used to adjust the median absolute deviation (MAD) for asymptotically normal consistency to the standard deviation. The default value based on [1], [2] is 1.4826.

Examples

>>> from sktime.transformations.scaledasinh import ScaledAsinhTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = ScaledAsinhTransformer ()
>>> y_hat = transformer. fit_transform (y)

References

  1. [1 ] (1, 2, 3, 4, 5, 6, 7) Ziel F, Weron R. Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks. Energy Economics. 2018 Feb 1;70:396-420. [2 ] (1, 2, 3) Uniejewski, B., Weron, R., Ziel, F., 2017. Variance stabilizing transformations for electricity spot price forecasting. IEEE Transactions on Power Systems, DOI: 10.1109/TPWRS.2017.2734563