class_docstr: AutoARIMAProphet adapter.
  
  Returns best ARIMA model using external variables created by the Prophet interface.
  This class receives as parameters the same as prophet.Prophet and uses a `models.AutoARIMA`
  backend.
  
  If your forecasting pipeline uses Prophet the `AutoARIMAProphet` adapter helps to
  easily substitute Prophet with an AutoARIMA.
  
  Parameters
  ----------
  growth : string
      'linear', 'logistic' or 'flat' to specify a linear, logistic or flat trend.
  changepoints : List of dates
      Potential changepoints. Otherwise selected automatically.
  n_changepoints : int
      Number of potential changepoints to include.
  changepoint_range : float
      Proportion of history in which trend changepoints will be estimated.
  yearly_seasonality : str, bool or int
      Fit yearly seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate.
  weekly_seasonality : str, bool or int
      Fit weekly seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate.
  daily_seasonality : str, bool or int
      Fit daily seasonality. Can be 'auto', True, False, or a number of Fourier terms to generate.
  holidays : pandas.DataFrame
      DataFrame with columns holiday (string) and ds (date type).
  interval_width : float
      Uncertainty forecast intervals width. `StatsForecast`'s level
  
  Notes
  -----
  You can create automated exogenous variables from the Prophet data processing pipeline
  these exogenous will be included into `AutoARIMA`'s exogenous features. Parameters like
  `seasonality_mode`, `seasonality_prior_scale`, `holidays_prior_scale`, `changepoint_prior_scale`,
  `mcmc_samples`, `uncertainty_samples`, `stan_backend` are Prophet exclusive.
  
  References
  ----------
  [Sean J. Taylor, Benjamin Letham (2017). "Prophet Forecasting at Scale"](https://peerj.com/preprints/3190.pdf)
  
  [Oskar Triebe, Hansika Hewamalage, Polina Pilyugina, Nikolay Laptev, Christoph Bergmeir, Ram Rajagopal (2021). "NeuralProphet: Explainable Forecasting at Scale".](https://arxiv.org/pdf/2111.15397.pdf)
  
  [Rob J. Hyndman, Yeasmin Khandakar (2008). "Automatic Time Series Forecasting: The forecast package for R"](https://www.jstatsoft.org/article/view/v027i03).
docstr: Fit the AutoARIMAProphet adapter.
  
  Parameters
  ----------
  df : pandas.DataFrame
      DataFrame with columns ds (date type) and y, the time series.
  disable_seasonal_features : bool (default=True)
      Disable Prophet's seasonal features.
  
  Returns
  -------
  AutoARIMAProphet
      Adapter object with `AutoARIMA` fitted model.
docstr_tok: ["Fit","the","AutoARIMAProphet","adapter",".","Parameters","--","--","--","--","--","df",":","pandas.DataFrame","DataFrame","with","columns","ds","(","date","type",")","and","y",",","the","time","series",".","disable_seasonal_features",":","bool","(","default=True",")","Disable","Prophet","'s","seasonal","features",".","Returns","--","--","--","-","AutoARIMAProphet","Adapter","object","with","`","AutoARIMA","`","fitted","model","."]
code: def fit(self, df, disable_seasonal_features=True):
      """Fit the AutoARIMAProphet adapter.
  
      Parameters
      ----------
      df : pandas.DataFrame
          DataFrame with columns ds (date type) and y, the time series.
      disable_seasonal_features : bool (default=True)
          Disable Prophet's seasonal features.
  
      Returns
      -------
      AutoARIMAProphet
          Adapter object with `AutoARIMA` fitted model.
      """
      if self.history is not None:
          raise Exception(
              "Prophet object can only be fit once. "
              "Instantiate a new object."
          )
      if ("ds" not in df) or ("y" not in df):
          raise ValueError(
              'Dataframe must have columns "ds" and "y" with the dates and '
              "values respectively."
          )
      history = df[df["y"].notnull()].copy()
      if history.shape[0] < 2:
          raise ValueError("Dataframe has less than 2 non-NaN rows.")
      self.history_dates = pd.to_datetime(
          pd.Series(df["ds"].unique(), name="ds")
      ).sort_values()
  
      history = self.setup_dataframe(history, initialize_scales=True)
      self.history = history
      self.set_auto_seasonalities()
      (
          seasonal_features,
          prior_scales,
          component_cols,
          modes,
      ) = self.make_all_seasonality_features(history)
      self.train_component_cols = component_cols
      self.component_modes = modes
      if disable_seasonal_features:
          seas = tuple(self.seasonalities.keys())
          seasonal_features = seasonal_features.loc[
              :, ~seasonal_features.columns.str.startswith(seas)
          ]
      self.xreg_cols = seasonal_features.columns
  
      y = history["y"].values
      X = (
          seasonal_features.values
          if not seasonal_features.empty
          else None
      )
      self.arima = self.arima.fit(y=y, X=X)
  
      return self
  
file_path: statsforecast/statsforecast/adapters/prophet.py
imports: import sys
  import pandas
  from ..arima import AutoARIMA
label_desc: Use image node_id 2 for calling the AutoARIMAProphet obj's underlying member method code with example usage: obj.fit(df, disable_seasonal_features) and returns: self