Transformer

ScaledAsinhTransformer

MultivariateInverse transformUnequal length

Hyperbolic sine transformation and its inverse [1].

Known as variance stabilizing transformation, Combined with an sktime.forecasting.compose.TransformedTargetForecaster, can be useful in time series that exhibit spikes [1], [2]

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] (1,2,3,4,5,6,7)

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