class_docstr: 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]_
  
  Parameters
  ----------
  mad_normalization_factor : float, 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.
  
  Attributes
  ----------
  shift_parameter_asinh_ : float
      shift parameter, denoted as "a" in [1]_, the median of sample data.
      It is fitted, based on the data provided in "fit".
  
  scale_parameter_asinh_ : float
      scale parameter, denoted as "b" in [1]_, the median absolute deviation
      (MAD) around the sample median adjusted by a factor for asymptotically
      normal consistency to the standard deviation (Based on [1]_, [2]_
      b = median_abs_deviation(sample data) :math:`\times` mad_normalization_factor).
      It is fitted, based on the data provided in "fit".
  
  See Also
  --------
  sktime.transformations.series.boxcox.LogTransformer :
      Transformer input data using natural log. Can help normalize data and
      compress variance of the series.
  sktime.transformations.series.boxcox.BoxCoxTransformer :
      Applies Box-Cox power transformation. Can help normalize data and
      compress variance of the series.
  sktime.transformations.series.exponent.ExponentTransformer :
      Transform input data by raising it to an exponent. Can help compress
      variance of series if a fractional exponent is supplied.
  sktime.transformations.series.exponent.SqrtTransformer :
      Transform input data by taking its square root. Can help compress
      variance of input series.
  
  Notes
  -----
  | The Hyperbolic Sine transformation is applied as:
  |   :math:`asinh(\frac{x- a}{b})`
  | The Hyperbolic Sine inverse transformation is applied as:
  |   :math:`b . sinh(x) + a`
  | where "a" is the shift parameter and "b" is the scale parameter [1]_.
  | a = median(sample data)
  | b = median_abs_deviation(sample data) :math:`\times` mad_normalization_factor
  
  References
  ----------
  .. [1] 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] 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
  
  Examples
  --------
  >>> from sktime.transformations.series.scaledasinh import ScaledAsinhTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()
  >>> transformer = ScaledAsinhTransformer()
  >>> y_hat = transformer.fit_transform(y)
code: def __init__(self, mad_normalization_factor=1.4826):
      super().__init__()
      self.mad_normalization_factor = mad_normalization_factor
  
file_path: sktime/sktime/transformations/series/scaledasinh.py
imports: import numpy
  from scipy import stats
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 1 to create a new ScaledAsinhTransformer object from inherited base classes: BaseTransformer with example: obj = ScaledAsinhTransformer(mad_normalization_factor)