WhiteNoiseAugmenter

class WhiteNoiseAugmenter(scale=1.0, random_state=42)[source]

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

If transform is given time series \(X={x_1, x_2, ... , x_n}\), then returns \(X_t={x_1+e_1, x_2+e_2, ..., x_n+e_n}\) where \(e_i\) are i.i.d. random draws from a normal distribution with mean \(\mu\) = 0 and standard deviation \(\sigma\) = scale. Time series augmentation by adding Gaussian Noise has been discussed among others in [1] and [2].

Parameters:

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]

Attributes:

is_fitted: Whether fit has been called.

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

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.