AutoCorrelationTransformer

class AutoCorrelationTransformer(adjusted=False, n_lags=None, fft=False, missing='none')[source]

Auto-correlation transformer.

The autocorrelation function measures how correlated a timeseries is with itself at different lags. The AutocorrelationTransformer returns these values as a series for each lag up to the n_lags specified.

Parameters:

adjustedbool, default=False

If True, then denominators for autocovariance are n-k, otherwise n.

n_lagsint, default=None

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

fftbool, default=False

If True, computes the ACF via FFT.

missing{“none”, “raise”, “conservative”, “drop”}, default=”none”

How missing values are to be treated in autocorrelation function calculations.

“none” performs no checks or handling of missing values
“raise” raises an exception if NaN values are found.
“drop” removes the missing observations and then estimates the autocovariances treating the 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. “n” in calculation is set to the number of non-missing observations.

Attributes:

is_fitted: Whether fit has been called.

Notes

Provides wrapper around statsmodels acf function.

Examples

>>> from sktime.transformations.acf import AutoCorrelationTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = AutoCorrelationTransformer(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.