STLBootstrapTransformer

Creates a population of similar time series.

Quickstart

from sktime.transformations.bootstrap import STLBootstrapTransformer

estimator = STLBootstrapTransformer(n_series: int=10, sp: int=12, block_length: int=None, sampling_replacement: bool=False, return_actual: bool=True, lambda_bounds: tuple=None, lambda_method: str='guerrero', seasonal: int=7, trend: int=None, low_pass: int=None, seasonal_deg: int=1, trend_deg: int=1, low_pass_deg: int=1, robust: bool=False, seasonal_jump: int=1, trend_jump: int=1, low_pass_jump: int=1, inner_iter: int=None, outer_iter: int=None, random_state: int | RandomState=None, return_indices=False)

Parameters(21)

n_seriesint, optional

The number of bootstrapped time series that will be generated, by default 10.

spint, optional

Seasonal periodicity of the data in integer form, by default 12. Must be an integer >= 2

block_lengthint, optional

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

Whether the MBB sample is with or without replacement, by default False.

return_actualbool, optional

If True the output will contain the actual time series, by default True. The actual time series will be labelled as “<series_name>_actual” (or “actual” if series name is None).

lambda_boundsTuple, optional

BoxCox parameter: Lower and upper bounds used to restrict the feasible range when solving for the value of lambda, by default None.

lambda_methodstr, optional

BoxCox parameter: {“pearsonr”, “mle”, “all”, “guerrero”}, by default “guerrero”. The optimization approach used to determine the lambda value used in the Box-Cox transformation.

seasonalint, optional

STL parameter: Length of the seasonal smoother. Must be an odd integer, and should normally be >= 7, by default 7.

trendint, optional

STL parameter: Length of the trend smoother, by default None. Must be an odd integer. If not provided uses the smallest odd integer greater than 1.5 * period / (1 - 1.5 / seasonal), following the suggestion in the original implementation.

low_passint, optional

STL parameter: Length of the low-pass filter, by default None. Must be an odd integer >=3. If not provided, uses the smallest odd integer > period

seasonal_degint, optional

STL parameter: Degree of seasonal LOESS. 0 (constant) or 1 (constant and trend), by default 1.

trend_degint, optional

STL parameter: Degree of trend LOESS. 0 (constant) or 1 (constant and trend), by default 1.

low_pass_degint, optional

STL parameter: Degree of low pass LOESS. 0 (constant) or 1 (constant and trend), by default 1.

robustbool, optional

STL parameter: Flag indicating whether to use a weighted version that is robust to some forms of outliers, by default False.

seasonal_jumpint, optional

STL parameter: Positive integer determining the linear interpolation step, by default 1. If larger than 1, the LOESS is used every seasonal_jump points and linear interpolation is between fitted points. Higher values reduce estimation time.

trend_jumpint, optional

STL parameter: Positive integer determining the linear interpolation step, by default 1. If larger than 1, the LOESS is used every trend_jump points and values between the two are linearly interpolated. Higher values reduce estimation time.

low_pass_jumpint, optional

STL parameter: Positive integer determining the linear interpolation step, by default 1. If larger than 1, the LOESS is used every low_pass_jump points and values between the two are linearly interpolated. Higher values reduce estimation time.

inner_iterint, optional

STL parameter: Number of iterations to perform in the inner loop, by default None. If not provided uses 2 if robust is True, or 5 if not. This param goes into STL.fit() from statsmodels.

outer_iterint, optional

STL parameter: Number of iterations to perform in the outer loop, by default None. If not provided uses 15 if robust is True, or 0 if not. This param goes into STL.fit() from statsmodels.

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

Controls the randomness of the estimator

return_indicesbool, optional

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

Examples

>>> from sktime.transformations.bootstrap import STLBootstrapTransformer
>>> from sktime.datasets import load_airline
>>> from sktime.utils.plotting import plot_series
>>> y = load_airline ()
>>> transformer = STLBootstrapTransformer (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 ] 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 [2 ] 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. [3 ] Kunsch HR (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17(3), 1217-1241 [4 ] https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html