SplitterBootstrapTransformer

Splitter based Bootstrapping method for synthetic time series generation.

Quickstart

from sktime.transformations.bootstrap import SplitterBootstrapTransformer

estimator = SplitterBootstrapTransformer(splitter=None, fold='train', shuffle=False, subsample=None, replace=True, random_state=None)

Parameters(6)

splitteroptional, sktime splitter, BaseSplitter descendant

default = SlidingWindowSplitter(window_length=3, step_length=1) The splitter used for the bootstrap splitting.

foldstr, one of “train” (default), “test”, and “both”

Determines which fold is returned as new instances in the panel. “train” - the training folds; “test” - the test folds; “both” - both training and test folds, and an additional string level with possible values "train" and "test" is present

shufflebool, default=False

whether to shuffle the order of folds uniformly at random before returning if not, folds will be returned in the ordering defined by the splitter

subsampleoptional, int or float, default = None

if provided, subsamples the folds returned uniformly at random int = subsample of that size will be returned (or full sample if smaller) float, must be between 0 and 1 = subsample of that fraction is returned Note: integer 1 selects one series; float 1 selects number in splitter many

replacebool, default=True; only used if subsample=True

whether sampling, if subsample is provided is with or without replacement True = with replacement, False = without replacement

random_stateint, np.random.RandomState or None (default)

Random seed for the estimator if None, numpy environment random seed is used if int, passed to numpy RandomState as seed if RandomState, will be used as random generator

Examples

>>> from sktime.transformations.bootstrap import SplitterBootstrapTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> transformer = SplitterBootstrapTransformer (fold = "both")
>>> y_hat = transformer. fit_transform (y)