ConformalIntervals

Empirical and conformal prediction intervals.

Quickstart

from sktime.forecasting.conformal import ConformalIntervals

estimator = ConformalIntervals(forecaster, method='empirical', initial_window=None, sample_frac=None, verbose=False, n_jobs=None)

Parameters(6)

forecasterestimator

Estimator to which probabilistic forecasts are being added

methodstr, optional, default=”empirical”

“empirical”: predictive interval bounds are empirical quantiles from training “empirical_residual”: upper/lower are plusminus (1-coverage)/2 quantiles

of the absolute residuals at horizon, i.e., of epsilon-h

“conformal_bonferroni”: Bonferroni, as in Stankeviciute et al

Caveat: this does not give frequentist but conformal predictive intervals

initial_windowfloat, int or None, optional (default=max(10, 0.1*len(y)))

Defines the size of the initial training window If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include for the initial window for the train split. If int, represents the relative number of train samples in the initial window. If None, the value is set to the larger of 0.1*len(y) and 10

sample_fracfloat, optional, default=None

value in range (0,1) corresponding to fraction of y index to calculate residuals matrix values for (for speeding up calculation)

verbosebool, optional, default=False

whether to print warnings if windows with too few data points occur

n_jobsint or None, optional, default=1

The number of jobs to run in parallel for fit. -1 means using all processors.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.conformal import ConformalIntervals
>>> from sktime.forecasting.naive import NaiveForecaster
>>> y = load_airline ()
>>> forecaster = NaiveForecaster (strategy = "drift")
>>> conformal_forecaster = ConformalIntervals (forecaster)
>>> conformal_forecaster. fit (y, fh = [1, 2, 3 ]) ConformalIntervals(
... )
>>> pred_int = conformal_forecaster. predict_interval () recommended use of ConformalIntervals together with ForecastingGridSearch is by 1. first running grid search, 2. then ConformalIntervals on the tuned params otherwise, nested sliding windows will cause high compute requirement
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.conformal import ConformalIntervals
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.model_selection import ForecastingGridSearchCV
>>> from sktime.split import ExpandingWindowSplitter
>>> from sktime.param_est.plugin import PluginParamsForecaster
>>> # part 1 = grid search
>>> cv = ExpandingWindowSplitter (fh = [1, 2, 3 ])
>>> forecaster = NaiveForecaster ()
>>> param_grid = { "strategy": ["last", "mean", "drift" ]}
>>> gscv = ForecastingGridSearchCV (
... forecaster = forecaster,
... param_grid = param_grid,
... cv = cv,
... )
>>> # part 2 = plug in results of grid search into conformal intervals estimator
>>> conformal_with_fallback = ConformalIntervals (NaiveForecaster ())
>>> gscv_with_conformal = PluginParamsForecaster (
... gscv,
... conformal_with_fallback,
... params = { "forecaster": "best_forecaster" },
... )
>>> y = load_airline ()
>>> gscv_with_conformal. fit (y, fh = [1, 2, 3 ]) PluginParamsForecaster(
... )
>>> y_pred_quantiles = gscv_with_conformal. predict_quantiles ()

References

1. [1 ] (1, 2, 3, 4, 5) Kamile Stankeviciute, Ahmed M Alaa and Mihaela van der Schaar. Conformal Time Series Forecasting. NeurIPS 2021.