Transformer

WhiteNoiseAugmenter

Augmenter adding Gaussian (i.e. white) noise to the time series.

Quickstart

python

from sktime.transformations.augmenter import WhiteNoiseAugmenter

estimator = WhiteNoiseAugmenter(scale=1.0, random_state=42)

Parameters(2)

scale: float, scale parameter (default=1.0): Specifies the standard deviation.
random_state: None or int or ``np.random.RandomState`` instance, optional: “If int or RandomState, use it for drawing the random variates. If None, rely on self.random_state. Default is None.” [3]

Examples

>>> import numpy as np
>>> from sktime.transformations.augmenter import WhiteNoiseAugmenter
>>> X = np. array ([1, 2, 3, 4, 5 ])
>>> augmenter = WhiteNoiseAugmenter (scale = 0.5, random_state = 42)
>>> augmenter. fit (X) WhiteNoiseAugmenter(
... )
>>> X_augmented = augmenter. transform (X) References and Footnotes ¶ [1]: WEN, Qingsong, et al. Time series data augmentation for deep learning: A survey. arXiv preprint arXiv:2002.12478, 2020. [2]: IWANA, Brian Kenji; UCHIDA, Seiichi. An empirical survey of data augmentation for time series classification with neural networks. Plos one, 2021, 16. Jg., Nr. 7, S. e0254841. [3]: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.rv_continuous.random_state.html