MovingBlockBootstrapTransformer
Moving Block Bootstrapping method for synthetic time series generation.
The Moving Block Bootstrapping (MBB) method introduced in [1] is can be used to create synthetic time series that mimic the autocorelation patterns of an observed stationary series. This method is frequently combined with other transformations e.g. BoxCox and STL to produce synthetic time series similar to the observed time series [2], [3].
The returned panel will be a multiindex dataframe (pd.DataFrame) with the series_id and time_index as the index and a single column of the time series value. The values for series_id are “actual” for the original series and “synthetic_n” (where n is an integer) for the generated series. See the Examples section for example output.
Schnellstart
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)Tags
Fähigkeiten
- Reihen ungleicher Länge
- Multivariat: Nicht unterstützt
- Inverse Transformation: Nicht unterstützt
- Fehlende Werte: Nicht unterstützt
- Entfernt fehlende Werte: Nicht unterstützt
- Gleicht Reihenlängen an: Nicht unterstützt
Eigenschaften
- Eingabetypscitype:transform-input
- Series
- Ausgabetypscitype:transform-output
- Panel
- Label-Typscitype:transform-labels
- None
- Fit ist leerfit_is_empty
- Ja
- Behält den Zeitindextransform-returns-same-time-index
- Nein
- Benötigt Xrequires_X
- Ja
- Benötigt yrequires_y
- Nein
- X und y brauchen denselben IndexX-y-must-have-same-index
- Nein
Parameter(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.
Beispiele
>>> 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 1949-05 121.0Referenzen
Kunsch HR (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17(3), 1217-1241
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
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.