class_docstr: 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_lags : int, default=None
      Number of lags to return partial autocorrelation for. If None,
      statsmodels acf function uses min(10 * np.log10(nobs), nobs // 2 - 1).
  
  method : str, 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.
  
  See Also
  --------
  AutoCorrelationTransformer
  
  Notes
  -----
  Provides wrapper around statsmodels
  `pacf <https://www.statsmodels.org/devel/generated/
  statsmodels.tsa.stattools.pacf.html>`_ function.
  
  
  Examples
  --------
  >>> from sktime.transformations.series.acf import PartialAutoCorrelationTransformer
  >>> from sktime.datasets import load_airline
  >>> y = load_airline()  # doctest: +SKIP
  >>> transformer = PartialAutoCorrelationTransformer(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 pacf
  
      # Passing an alpha values other than None would return confidence intervals
      # and break the signature of the series-to-series transformer
      zt = pacf(X, nlags=self.n_lags, method=self.method, alpha=None)
      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 PartialAutoCorrelationTransformer obj's underlying member method code with example usage: obj._transform(X, y) and returns: pd