AcorrLjungbox

class AcorrLjungbox(lags=1, boxpierce=False)[source]

Ljung-Box test of autocorrelation.

Direct interface to statsmodels.stats.diagnostic.acorr_ljungbox.

Tests whether the autocorrelation of a time series is significantly different from zero at given lags.

Parameters:

lagsint or array_like, default=None

If lags is an integer then this is taken to be the largest lag that is included.
If lags is an array, then all lags are included upto largest lag in the list.
If lags is None, then the default maxlag is min(10, nobs // 5), where nobs is the length of the time series passed in fit. The default number of lags changes if period is set.

boxpiercebool, default=False

If true, then Box-Pierce test results are also returned.

Attributes:

lb_statistic_pd.Series: The Ljung-Box test statistic, for each lag.
lb_pvalue_pd.Series: The p-value based on chi-square distribution. The p-value is computed as: 1 - chi2.cdf(lb_stat, dof) where dof is lag - model_df. If lag - model_df <= 0, then NaN is returned for the pvalue.
bp_stat_pd.Series: The Box-Pierce test statistic, for each lag. Only returned if boxpierce is True.
bp_pvalue_pd.Series: The p-value based for Box-Pierce test on chi-square distribution, computed as: 1 - chi2.cdf(bp_stat, dof) where dof is lag - model_df. If lag - model_df <= 0, then NaN is returned for the pvalue. Only returned if boxpierce is True.

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.param_est.lag import AcorrLjungbox
>>>
>>> X = load_airline()
>>> lag_est = AcorrLjungbox()
>>> lag_est.fit(X)
AcorrLjungbox(...)

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.