class_docstr: 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
  ----------
  adjusted : bool, default=False
      If True, then denominators for autocovariance are n-k, otherwise n.
  
  n_lags : int, default=None
      Number of lags to return autocorrelation for. If None,
      statsmodels acf function uses min(10 * np.log10(nobs), nobs - 1).
  
  fft : bool, 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.
  
  See Also
  --------
  PartialAutoCorrelationTransformer
  
  Notes
  -----
  Provides wrapper around statsmodels
  `acf <https://www.statsmodels.org/devel/generated/
  statsmodels.tsa.stattools.acf.html>`_ function.
  
  Examples
  --------
  >>> from sktime.transformations.series.acf import AutoCorrelationTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()  # doctest: +SKIP
  >>> transformer = AutoCorrelationTransformer(n_lags=12)  # doctest: +SKIP
  >>> y_hat = transformer.fit_transform(y)  # doctest: +SKIP
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
  
  
  Returns
  -------
  params : dict or list of dict, default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
  
  
      Returns
      -------
      params : dict or list of dict, default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      return [{}, {"n_lags": 1}]
  
file_path: sktime/sktime/transformations/series/acf.py
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the AutoCorrelationTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list