HampelFilter

class HampelFilter(window_length=10, n_sigma=3, k=1.4826, return_bool=False)[source]

Use HampelFilter to detect outliers based on a sliding window.

Correction of outliers is recommended by means of the sktime.Imputer, so both can be tuned separately.

Parameters:

window_lengthint, optional (default=10): Length of the sliding window
n_sigmaint, optional (default=3): Defines how strong a point must outly to be an “outlier”
kfloat, optional (default = 1.4826): A constant scale factor which is dependent on the distribution, for Gaussian it is approximately 1.4826, by default 1.4826
return_boolbool, optional (default=False): If True, outliers are filled with True and non-outliers with False. Else, outliers are filled with np.nan.

Attributes:

is_fitted: Whether fit has been called.

Notes

Implementation is based on [1].

References

[1]

Hampel F. R., “The influence curve and its role in robust estimation”, Journal of the American Statistical Association, 69, 382-393, 1974

Examples

>>> from sktime.transformations.outlier_detection import HampelFilter
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = HampelFilter(window_length=10)
>>> y_hat = transformer.fit_transform(y)

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.