class_docstr: A pipeline of transformers with an optional final estimator stage
  
  The pipeline object chains together an arbitrary number of named, ordered
  transformations, passing the output from one as the input to the next. As
  the last stage, an ``ARIMA`` or ``AutoARIMA`` object will be fit. This
  pipeline takes after the scikit-learn ``sklearn.Pipeline`` object, which
  behaves similarly but does not share the same time-series interface
  that ``pmdarima`` follows.
  
  The purpose of the pipeline is to assemble several steps that can be
  cross-validated together while setting different parameters.
  For this, it enables setting parameters of the various steps using their
  names and the parameter name separated by a `'__'`, as in the example
  below.
  
  Parameters
  ----------
  steps : list
      List of (name, transform) tuples (implementing fit/transform) that are
      chained, in the order in which they are chained, with the last object
      an ARIMA or AutoARIMA estimator.
  
  Examples
  --------
  >>> from pmdarima.datasets import load_wineind
  >>> from pmdarima.arima import AutoARIMA
  >>> from pmdarima.pipeline import Pipeline
  >>> from pmdarima.preprocessing import FourierFeaturizer
  >>>
  >>> wineind = load_wineind()
  >>> pipeline = Pipeline([
  ...     ("fourier", FourierFeaturizer(m=12, k=3)),
  ...     ("arima", AutoARIMA(seasonal=False, stepwise=True,
  ...                         suppress_warnings=True,
  ...                         error_action='ignore'))
  ... ])
  >>> pipeline.fit(wineind)
  Pipeline(steps=[('fourier', FourierFeaturizer(k=3, m=12)),
                  ('arima', AutoARIMA(D=None, alpha=0.05, callback=None,
                                      d=None, disp=0, error_action='ignore',
                                      information_criterion='aic', m=1,
                                      max_D=1, max_P=2, max_Q=2, max_d=2,
                                      max_order=10, max_p=5, max_q=5,
                                      maxiter=None, method=None,
                                      n_fits=10, n...s_warnings=True,
                                      test='kpss', trace=False,
                                      transparams=True, trend=None,
                                      with_intercept=True))])
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docstr: Forecast future (transformed) values
  
  Generate predictions (forecasts) ``n_periods`` in the future.
  Note that if an ``X`` array was used in the model fit, it
  will be expected for the predict procedure and will fail otherwise.
  Forecasts may be transformed by the endogenous steps along the way and
  might be on a different scale than raw training/test data.
  
  Parameters
  ----------
  n_periods : int, optional (default=10)
      The number of periods in the future to forecast.
  
  X : array-like, shape=[n_obs, n_vars], optional (default=None)
      An optional 2-d array of exogenous variables. If provided, these
      variables are used as additional features in the regression
      operation. This should not include a constant or trend. Note that
      if an ``ARIMA`` is fit on exogenous features, it must be provided
      exogenous features for making predictions.
  
  return_conf_int : bool, optional (default=False)
      Whether to get the confidence intervals of the forecasts.
  
  alpha : float, optional (default=0.05)
      The confidence intervals for the forecasts are (1 - alpha) %
  
  inverse_transform : bool, optional (default=True)
      Whether to inverse transform predictions, if they are in log or
      BoxCox scale. Any endog transformer will be inverse-transformed.
  
  **kwargs : keyword args
      Extra keyword arguments used for each stage's ``transform`` stage
      and the estimator's ``predict`` stage. Similar to scikit-learn
      pipeline keyword args, the keys are compound, comprised of the
      stage name and the argument name separated by a "__". For instance,
      if you have a FourierFeaturizer whose stage is named
      "fourier", your transform kwargs could resemble::
  
          {"fourier__n_periods": 50}
  
  Returns
  -------
  forecasts : array-like, shape=(n_periods,)
      The array of transformed, forecasted values.
  
  conf_int : array-like, shape=(n_periods, 2), optional
      The confidence intervals for the forecasts. Only returned if
      ``return_conf_int`` is True.
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code: def predict(
      self,
      n_periods=10,
      X=None,
      return_conf_int=False,
      alpha=0.05,
      inverse_transform=True,
      **kwargs,
  ):
      """Forecast future (transformed) values
  
      Generate predictions (forecasts) ``n_periods`` in the future.
      Note that if an ``X`` array was used in the model fit, it
      will be expected for the predict procedure and will fail otherwise.
      Forecasts may be transformed by the endogenous steps along the way and
      might be on a different scale than raw training/test data.
  
      Parameters
      ----------
      n_periods : int, optional (default=10)
          The number of periods in the future to forecast.
  
      X : array-like, shape=[n_obs, n_vars], optional (default=None)
          An optional 2-d array of exogenous variables. If provided, these
          variables are used as additional features in the regression
          operation. This should not include a constant or trend. Note that
          if an ``ARIMA`` is fit on exogenous features, it must be provided
          exogenous features for making predictions.
  
      return_conf_int : bool, optional (default=False)
          Whether to get the confidence intervals of the forecasts.
  
      alpha : float, optional (default=0.05)
          The confidence intervals for the forecasts are (1 - alpha) %
  
      inverse_transform : bool, optional (default=True)
          Whether to inverse transform predictions, if they are in log or
          BoxCox scale. Any endog transformer will be inverse-transformed.
  
      **kwargs : keyword args
          Extra keyword arguments used for each stage's ``transform`` stage
          and the estimator's ``predict`` stage. Similar to scikit-learn
          pipeline keyword args, the keys are compound, comprised of the
          stage name and the argument name separated by a "__". For instance,
          if you have a FourierFeaturizer whose stage is named
          "fourier", your transform kwargs could resemble::
  
              {"fourier__n_periods": 50}
  
      Returns
      -------
      forecasts : array-like, shape=(n_periods,)
          The array of transformed, forecasted values.
  
      conf_int : array-like, shape=(n_periods, 2), optional
          The confidence intervals for the forecasts. Only returned if
          ``return_conf_int`` is True.
      """
      n_periods = self._check_n_periods(n_periods, X)
      kwargs = _warn_for_deprecated(**kwargs)
      Xt, est, predict_kwargs = self._pre_predict(
          n_periods, X, **kwargs
      )
  
      return_vals = est.predict(
          n_periods=n_periods,
          X=Xt,
          return_conf_int=return_conf_int,
          alpha=alpha,
          **predict_kwargs,
      )
  
      return self._post_predict(
          Xt, return_vals, return_conf_int, inverse_transform
      )
  
imports: from itertools import islice
  import pandas
  import warnings
  from sklearn.base import BaseEstimator, clone
  from .base import BaseARIMA
  from .preprocessing.base import BaseTransformer
  from .preprocessing.endog.base import BaseEndogTransformer
  from .preprocessing.exog.base import BaseExogTransformer, BaseExogFeaturizer
  from .utils import check_endog
  from .compat import DTYPE, check_is_fitted
  from .compat.sklearn import if_delegate_has_method
label_desc: Use image node_id 14 for calling the Pipeline obj's underlying member method code with example usage: obj.predict(n_periods, X, return_conf_int, alpha, inverse_transform) and returns: self