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: Transform X and return a transformed version.
  
  private _transform containing the core logic, called from transform
  
  Parameters
  ----------
  X : pd.Series
      Data to be transformed
  y : ignored argument for interface compatibility
      Additional data, e.g., labels for transformation
  
  Returns
  -------
  transformed version of X
code: def _transform(self, X, y=None):
      """Transform X and return a transformed version.
  
      private _transform containing the core logic, called from transform
  
      Parameters
      ----------
      X : pd.Series
          Data to be transformed
      y : ignored argument for interface compatibility
          Additional data, e.g., labels for transformation
  
      Returns
      -------
      transformed version of X
      """
      from statsmodels.tsa.stattools import acf
  
      # Passing an alpha values other than None would return confidence intervals
      # and break the signature of the series-to-series transformer
      zt = acf(
          X,
          adjusted=self.adjusted,
          nlags=self.n_lags,
          qstat=False,
          fft=self.fft,
          alpha=None,
          missing=self.missing,
      )
      return pd.Series(zt)
  
file_path: sktime/sktime/transformations/series/acf.py
imports: import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 2 for calling the AutoCorrelationTransformer obj's underlying member method code with example usage: obj._transform(X, y) and returns: pd