ScaledLogitTransformer

Scaled logit transform or Log transform.

Quickstart

from sktime.transformations.scaledlogit import ScaledLogitTransformer

estimator = ScaledLogitTransformer(lower_bound=None, upper_bound=None)

Parameters(2)

lower_boundfloat, optional, default=None

lower bound of inverse transform function

upper_boundfloat, optional, default=None

upper bound of inverse transform function

Examples

>>> import numpy as np
>>> from sktime.datasets import load_airline
>>> from sktime.transformations.scaledlogit import ScaledLogitTransformer
>>> from sktime.forecasting.trend import PolynomialTrendForecaster
>>> from sktime.forecasting.compose import TransformedTargetForecaster
>>> y = load_airline ()
>>> fcaster = TransformedTargetForecaster ([
... ("scaled_logit", ScaledLogitTransformer (0, 650)),
... ("poly", PolynomialTrendForecaster (degree = 2))
... ])
>>> fcaster. fit (y) TransformedTargetForecaster(
... )
>>> y_pred = fcaster. predict (fh = np. arange (32))

References

1. [1 ] Hyndsight - Forecasting within limits: https://robjhyndman.com/hyndsight/forecasting-within-limits/ [2 ] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3. Accessed on January 24th 2022.