SeasonalityACF

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

Find candidate seasonality parameter using autocorrelation function CI.

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 confidence intervals at a significance level, and returns lags with significant positive auto-correlation, ordered by lower confidence limit.

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
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 SeasonalityACF
>>>
>>> X = load_airline().diff()[1:]
>>> sp_est = SeasonalityACF()
>>> sp_est.fit(X)
SeasonalityACF(...)
>>> sp_est.get_fitted_params()["sp"]
12
>>> sp_est.get_fitted_params()["sp_significant"]
array([12, 11])

Series should be stationary before applying ACF. To pipeline SeasonalityACF with the Differencer, use the ParamFitterPipeline:

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.seasonality import SeasonalityACF
>>> from sktime.transformations.difference import Differencer
>>>
>>> X = load_airline()
>>> sp_est = Differencer() * SeasonalityACF()
>>> sp_est.fit(X)
ParamFitterPipeline(...)
>>> sp_est.get_fitted_params()["sp"]
12
>>> sp_est.get_fitted_params()["sp_significant"]
array([12, 11])

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.