MovingBlockBootstrapTransformer

Moving Block Bootstrapping method for synthetic time series generation.

Quickstart

from sktime.transformations.bootstrap import MovingBlockBootstrapTransformer

estimator = MovingBlockBootstrapTransformer(n_series: int=10, block_length: int=10, sampling_replacement: bool=False, return_actual: bool=True, random_state: int | RandomState=None, return_indices=False)

Parameters(6)

n_seriesint, optional, default=10

The number of bootstrapped time series that will be generated

block_lengthint, optional, default = min(2*sp, len(X) - sp)

The length of the block in the MBB method, by default None. If not provided, the following heuristic is used, the block length will the minimum between 2*sp and len(X) - sp.

sampling_replacementbool, optional, default=False

Whether the MBB sample is with or without replacement

return_actualbool, optional, default=True

If True the output will contain the actual time series. The actual time series will be labelled as “actual”.

random_stateint, np.random.RandomState or None, by default None

Controls the randomness of the estimator

return_indicesbool, optional, default=False.

If True, the output will contain the resampled indices as extra column.

Examples

>>> from sktime.transformations.bootstrap import MovingBlockBootstrapTransformer
>>> from sktime.datasets import load_airline
>>> from sktime.utils.plotting import plot_series
>>> y = load_airline ()
>>> transformer = MovingBlockBootstrapTransformer (10)
>>> y_hat = transformer. fit_transform (y)
>>> series_list = []
>>> names = []
>>> for group, series in y_hat. groupby (level = [0 ], as_index = False):
... series. index = series. index. droplevel (0)
... series_list. append (series)
... names. append (group)
>>> plot_series (* series_list, labels = names) (
... )
>>> print (y_hat. head ()) Number of airline passengers series_id time_index actual 1949-01 112.0 1949-02 118.0 1949-03 132.0 1949-04 129.0 1949-05 121.0

References

1. [1 ] Kunsch HR (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17(3), 1217-1241 [2 ] Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303-312 [3 ] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3, Chapter 12.5. Accessed on February 13th 2022. Accessed on February 13th 2022.