SeasonalityACFqstat

class SeasonalityACFqstat(candidate_sp=None, p_threshold=0.05, p_adjust='fdr_by', adjusted=False, nlags=None, fft=True, missing='none')[source]

Find candidate seasonality parameter using autocorrelation function LB q-stat.

Uses statsmodels.tsa.stattools.acf for computing the autocorrelation function, and uses its testing functionality to determine candidate seasonality parameters. (“seasonality parameter” are integer lags, and abbreviated by sp, below)

Obtains Ljung-Box q-statistic to test for candidate sp at candidate_sp.

Then applies statsmodels.stats.multitest.multipletests to correct multiple tests. Fitted attributes returned are significant sp and the most significant sp. These can be used in conditional or unconditional deseasonalization.

Note: this should be applied to stationary series.: Quick stationarity transformation can be achieved by differencing. See also: Differencer

Parameters:

candidate_spNone, int or list of int, optional, default = None: candidate sp to test, and to restrict tests to; ints must be 2 or larger if None, will test all integer lags between 2 and nlags (inclusive)
p_thresholdfloat, optional, default=0.05: significance threshold to apply in testing for seasonality
p_adjuststr, optional, default=”fdr_by” (Benjamini/Yekutieli): multiple testing correction applied to p-values of candidate sp in acf test multiple testing correction is applied to Ljung-Box tests on candidate_sp values can be “none” or strings accepted by statsmodels multipletests “none” = no multiple testing correction is applied, raw p-values are used “fdr_by” = Benjamini-Yekutieli FDR control procedure for other possible strings, see statsmodels.stats.multitest.multipletests
adjustedbool, optional, default=False: If True, then denominators for autocovariance are n-k, otherwise n.
nlagsint, optional, default=None: Number of lags to compute autocorrelations for and select from. At default None, uses min(10 * np.log10(nobs), nobs - 1). Will be ignored if candidate_sp is provided.
fftbool, optional, default=True: If True, computes the ACF via FFT.
missingstr, [“none”, “raise”, “conservative”, “drop”], optional, default=”none”: Specifies how NaNs are to be treated. “none” performs no checks. “raise” raises an exception if NaN values are found. “drop” removes the missing observations and treats non-missing as contiguous. “conservative” computes the autocovariance using nan-ops so that nans are

removed when computing the mean and cross-products that are used to estimate the autocovariance. When using “conservative”, n is set to the number of non-missing observations.

Attributes:

sp_int, seasonality period at lowest p-level, if any sub-threshold, else 1: if candidate_sp is passed, will be in candidate_sp or 1
sp_significant_list of int, seasonality periods with sub-threshold p-levels: ordered increasingly by p-level. Empty list, not [1], if none are sub-threshold

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.seasonality import SeasonalityACFqstat
>>> X = load_airline().diff()[1:]
>>> sp_est = SeasonalityACFqstat(candidate_sp=[3, 7, 12])
>>> sp_est.fit(X)
SeasonalityACFqstat(...)
>>> sp_est.get_fitted_params()["sp_significant"]
array([12,  7,  3])

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.
`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.