ScaledAsinhTransformer

ScaledAsinhTransformer

class ScaledAsinhTransformer(mad_normalization_factor=1.4826)[source]

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_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.

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) \(\times\) mad_normalization_factor). It is fitted, based on the data provided in “fit”.

See also

sktime.transformations.boxcox.LogTransformer

Transformer input data using natural log. Can help normalize data and compress variance of the series.

sktime.transformations.boxcox.BoxCoxTransformer

Applies Box-Cox power transformation. Can help normalize data and compress variance of the series.

sktime.transformations.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.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:

\(asinh(\frac{x- a}{b})\)

The Hyperbolic Sine inverse transformation is applied as:

\(b . sinh(x) + a\)

where “a” is the shift parameter and “b” is the scale parameter [1].

a = median(sample data)

b = median_abs_deviation(sample data) \(\times\) mad_normalization_factor

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

Examples

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

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.