SeasonalityPeriodogram

SeasonalityPeriodogram

class SeasonalityPeriodogram(min_period=4, max_period=None, thresh=0.1)[source]

Score periodicities by their spectral power.

Computes seasonality periodogram based on iloc indices (not loc labels), and finds significant periods based on their spectral power, using Welch’s method of periodogram averaging [R7c18f055916d-1].

Computes significant periods based on a threshold of the maximum power, i.e., periods with power above thresh * maxpower are considered significant, and the one with highest power is considered the main seasonality period.

Significance is determined by thresholding as above, not by statistical testing.

Based on seasonal package by welch [R7c18f055916d-2].

Parameters:

min_periodint

Disregard periods shorter than this number of samples. Defaults to 4

max_periodint

Disregard periods longer than this number of samples. Defaults to None

threshfloat (0..1)

Retain periods scoring above thresh*maxscore. Defaults to 0.10

Attributes:

sp_int, seasonality period with highest power, if any sub-threshold, else 1

sp_significant_list of int, array of Fourier periods in descending order

of their powers.

References

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.seasonality import SeasonalityPeriodogram
>>> X = load_airline().diff()[1:]
>>> sp_est = SeasonalityPeriodogram()
>>> sp_est.fit(X)
SeasonalityPeriodogram(...)
>>> sp_est.get_fitted_params()["sp"]
6
>>> sp_est.get_fitted_params()["sp_significant"]
array([ 6, 12, 14,  4, 10,  5])

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 estimator and estimate parameters.
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.
periodogram(data[, min_period, max_period])	Score periodicities by their spectral power.
periodogram_peaks(data[, min_period, ...])	Return a list of intervals containing high-scoring periods.
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.
update(X[, y])	Update fitted parameters on more data.