PartialAutoCorrelationTransformer

class PartialAutoCorrelationTransformer(n_lags=None, method='ywadjusted')[source]

Partial auto-correlation transformer.

The partial autocorrelation function measures the conditional correlation between a timeseries and its self at different lags. In particular, the correlation between a time period and a lag, is calculated conditional on all the points between the time period and the lag.

The PartialAutoCorrelationTransformer returns these values as a series for each lag up to the n_lags specified.

Parameters:

n_lagsint, default=None

Number of lags to return partial autocorrelation for. If None, statsmodels acf function uses min(10 * np.log10(nobs), nobs // 2 - 1).

methodstr, default=”ywadjusted”

Specifies which method for the calculations to use.

“yw” or “ywadjusted” : Yule-Walker with sample-size adjustment in denominator for acovf. Default.
“ywm” or “ywmle” : Yule-Walker without adjustment.
“ols” : regression of time series on lags of it and on constant.
“ols-inefficient” : regression of time series on lags using a single common sample to estimate all pacf coefficients.
“ols-adjusted” : regression of time series on lags with a bias adjustment.
“ld” or “ldadjusted” : Levinson-Durbin recursion with bias correction.
“ldb” or “ldbiased” : Levinson-Durbin recursion without bias correction.

Attributes:

is_fitted: Whether fit has been called.

Notes

Provides wrapper around statsmodels pacf function.

Examples

>>> from sktime.transformations.acf import PartialAutoCorrelationTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = PartialAutoCorrelationTransformer(n_lags=12)
>>> y_hat = transformer.fit_transform(y)

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 transformer to X, optionally to y.
`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.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`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.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.