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))])
class_docstr_tok: ["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",")",")","]",")"]
docstr: Update an ARIMA or auto-ARIMA as well as any necessary transformers
  
  Passes the newly observed values through the appropriate endog
  transformations, and the ``X`` array through the exog transformers
  (updating where necessary) before finally updating the ARIMA model.
  
  Parameters
  ----------
  y : array-like or iterable, shape=(n_samples,)
      The time-series data to add to the endogenous samples on which the
      ``ARIMA`` estimator was previously fit. This may either be a Pandas
      ``Series`` object or a numpy array. This should be a one-
      dimensional array of finite floats.
  
  X : array-like, shape=[n_obs, n_vars], optional (default=None)
      An optional 2-d array of exogenous variables. If the model was
      fit with an exogenous array of covariates, it will be required for
      updating the observed values.
  
  maxiter : int, optional (default=None)
      The number of iterations to perform when updating the model. If
      None, will perform ``max(5, n_samples // 10)`` iterations.
  
  **kwargs : keyword args
      Extra keyword arguments used for each stage's ``update`` stage.
      Similar to scikit-learn pipeline keyword args, the keys are
      compound, comprised of the stage name and the argument name
      separated by a "__".
docstr_tok: ["Update","an","ARIMA","or","auto-ARIMA","as","well","as","any","necessary","transformers","Passes","the","newly","observed","values","through","the","appropriate","endog","transformations",",","and","the","``","X","``","array","through","the","exog","transformers","(","updating","where","necessary",")","before","finally","updating","the","ARIMA","model",".","Parameters","--","--","--","--","--","y",":","array-like","or","iterable",",","shape=","(","n_samples",",",")","The","time-series","data","to","add","to","the","endogenous","samples","on","which","the","``","ARIMA","``","estimator","was","previously","fit",".","This","may","either","be","a","Pandas","``","Series","``","object","or","a","numpy","array",".","This","should","be","a","one-","dimensional","array","of","finite","floats",".","X",":","array-like",",","shape=","[","n_obs",",","n_vars","]",",","optional","(","default=None",")","An","optional","2-d","array","of","exogenous","variables",".","If","the","model","was","fit","with","an","exogenous","array","of","covariates",",","it","will","be","required","for","updating","the","observed","values",".","maxiter",":","int",",","optional","(","default=None",")","The","number","of","iterations","to","perform","when","updating","the","model",".","If","None",",","will","perform","``","max","(","5",",","n_samples","\/\/","10",")","``","iterations",".","*","*","kwargs",":","keyword","args","Extra","keyword","arguments","used","for","each","stage","'s","``","update","``","stage",".","Similar","to","scikit-learn","pipeline","keyword","args",",","the","keys","are","compound",",","comprised","of","the","stage","name","and","the","argument","name","separated","by","a","``","__","''","."]
code: def update(self, y, X=None, maxiter=None, **kwargs):
      """Update an ARIMA or auto-ARIMA as well as any necessary transformers
  
      Passes the newly observed values through the appropriate endog
      transformations, and the ``X`` array through the exog transformers
      (updating where necessary) before finally updating the ARIMA model.
  
      Parameters
      ----------
      y : array-like or iterable, shape=(n_samples,)
          The time-series data to add to the endogenous samples on which the
          ``ARIMA`` estimator was previously fit. This may either be a Pandas
          ``Series`` object or a numpy array. This should be a one-
          dimensional array of finite floats.
  
      X : array-like, shape=[n_obs, n_vars], optional (default=None)
          An optional 2-d array of exogenous variables. If the model was
          fit with an exogenous array of covariates, it will be required for
          updating the observed values.
  
      maxiter : int, optional (default=None)
          The number of iterations to perform when updating the model. If
          None, will perform ``max(5, n_samples // 10)`` iterations.
  
      **kwargs : keyword args
          Extra keyword arguments used for each stage's ``update`` stage.
          Similar to scikit-learn pipeline keyword args, the keys are
          compound, comprised of the stage name and the argument name
          separated by a "__".
      """
      check_is_fitted(self, "steps_")
  
      # Push the arrays through all of the transformer steps that have the
      # appropriate update_and_transform method
      yt = y
      Xt = X
      named_kwargs = self._get_kwargs(**kwargs)
  
      for step_idx, name, transformer in self._iter(with_final=False):
          kw = named_kwargs[name]
          if hasattr(transformer, "update_and_transform"):
              yt, Xt = transformer.update_and_transform(
                  y=yt, X=Xt, **kw
              )
          else:
              yt, Xt = transformer.transform(yt, Xt, **kw)
  
      if self.x_feats_ is not None:
          Xt = Xt[self.x_feats_]
  
      # Now we can update the arima
      nm, est = self.steps_[-1]
      return est.update(yt, X=Xt, maxiter=maxiter, **named_kwargs[nm])
  
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 17 for calling the Pipeline obj's underlying member method code with example usage: obj.update(y, X, maxiter) and returns: est