SquaringResiduals

Compute the prediction variance based on a separate forecaster.

Quickstart

from sktime.forecasting.squaring_residuals import SquaringResiduals

estimator = SquaringResiduals(forecaster=None, residual_forecaster=None, initial_window=5, strategy='square', distr='norm', distr_kwargs=None)

Parameters(7)

forecastersktime forecaster, BaseForecaster descendant, optional

Estimator to which probabilistic forecasts are being added Default = NaiveForecaster()

residual_forecastersktime forecaster, BaseForecaster descendant, optional

Estimator which is fitted to the residuals of forecaster Default = NaiveForecaster()

initial_windowint, optional, default=2

Size of initial_window to which forecaster is fitted

steps_aheadint, optional, default=1

Steps ahead for which we predict the residuals

strategystr, optional, default=’square’

Function applied to the residuals

distrstr, optional, default=’norm’

Distributional assumption ([“norm”, “laplace”, “t”, “cauchy”])

distr_kwargsdict, optional

Additional arguments required by the distribution

Examples

>>> from sktime.datasets import load_macroeconomic
>>> from sktime.forecasting.base import ForecastingHorizon
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.theta import ThetaForecaster
>>> from sktime.forecasting.squaring_residuals import SquaringResiduals
>>> fc = NaiveForecaster ()
>>> var_fc = ThetaForecaster ()
>>> y = load_macroeconomic (). realgdp
>>> sqr = SquaringResiduals (forecaster = fc, residual_forecaster = var_fc)
... >>> fh = ForecastingHorizon (values = [1, 2, 3 ])
>>> sqr = sqr. fit (y, fh = fh)
>>> pred_interval = sqr. predict_interval (coverage = 0.95)