HampelDetector

HampelDetector

class HampelDetector(window_length=10, n_sigma=3.0, k=1.4826)[source]

Anomaly detector based on Hampel filter described in [1].

Parameters:

window_lengthint, optional (default=10)

The size of the sliding window (number of observations).

n_sigmafloat, optional (default=3.0)

The number of standard deviations to use for the outlier threshold.

kfloat, optional (default=1.4826)

The consistency constant which depends on the underlying distribution. By default, we choose k=1.4826 - the value for Gaussian distribution.

Attributes:

is_fitted

Whether fit has been called.

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.detection.hampel import HampelDetector
>>> detector = HampelDetector()
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> detector.fit(y)
HampelDetector()
>>> y_anomalies = detector.predict(y)
>>> anomaly_scores = detector.transform_scores(y)

Methods

change_points_to_segments(y_sparse[, start, end])	Convert an series of change point indexes to segments.
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.
dense_to_sparse(y_dense)	Convert the dense output from an detector to a sparse format.
fit(X[, y])	Fit to training data.
fit_predict(X[, y])	Fit to data, then predict it.
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.
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.
predict(X)	Create labels on test/deployment data.
predict_points(X)	Predict changepoints/anomalies on test/deployment data.
predict_scores(X)	Return scores for predicted labels on test/deployment data.
predict_segments(X)	Predict segments on test/deployment data.
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.
segments_to_change_points(y_sparse)	Convert segments to change points.
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.
sparse_to_dense(y_sparse, index)	Convert the sparse output from an detector to a dense format.
transform(X)	Create labels on test/deployment data.
transform_scores(X)	Return scores for predicted labels on test/deployment data.
update(X[, y])	Update model with new data and optional ground truth labels.
update_predict(X[, y])	Update model with new data and create labels for it.