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).
code: def __init__(
      self,
      growth="linear",
      changepoints=None,
      n_changepoints=25,
      changepoint_range=0.8,
      yearly_seasonality="auto",
      weekly_seasonality="auto",
      daily_seasonality="auto",
      holidays=None,
      seasonality_mode="additive",
      seasonality_prior_scale=10.0,
      holidays_prior_scale=10.0,
      changepoint_prior_scale=0.05,
      mcmc_samples=0,
      interval_width=0.80,
      uncertainty_samples=1000,
      stan_backend=None,
      d=None,
      D=None,
      max_p=5,
      max_q=5,
      max_P=2,
      max_Q=2,
      max_order=5,
      max_d=2,
      max_D=1,
      start_p=2,
      start_q=2,
      start_P=1,
      start_Q=1,
      stationary=False,
      seasonal=True,
      ic="aicc",
      stepwise=True,
      nmodels=94,
      trace=False,
      approximation=False,
      method=None,
      truncate=None,
      test="kpss",
      test_kwargs=None,
      seasonal_test="seas",
      seasonal_test_kwargs=None,
      allowdrift=False,
      allowmean=False,
      blambda=None,
      biasadj=False,
      period=1,
  ):
      Prophet.__init__(
          self,
          growth,
          changepoints,
          n_changepoints,
          changepoint_range,
          yearly_seasonality,
          weekly_seasonality,
          daily_seasonality,
          holidays,
          seasonality_mode,
          seasonality_prior_scale,
          holidays_prior_scale,
          changepoint_prior_scale,
          mcmc_samples,
          interval_width,
          uncertainty_samples,
          stan_backend,
      )
      self.arima = AutoARIMA(
          d=d,
          D=D,
          max_p=max_p,
          max_q=max_q,
          max_P=max_P,
          max_Q=max_Q,
          max_order=max_order,
          max_d=max_d,
          max_D=max_D,
          start_p=start_p,
          start_q=start_q,
          start_P=start_P,
          start_Q=start_Q,
          stationary=stationary,
          seasonal=seasonal,
          ic=ic,
          stepwise=stepwise,
          nmodels=nmodels,
          trace=trace,
          approximation=approximation,
          method=method,
          truncate=truncate,
          test=test,
          test_kwargs=test_kwargs,
          seasonal_test=seasonal_test,
          seasonal_test_kwargs=seasonal_test_kwargs,
          allowdrift=allowdrift,
          allowmean=allowmean,
          blambda=blambda,
          biasadj=biasadj,
          period=period,
      )
  
file_path: statsforecast/statsforecast/adapters/prophet.py
imports: import sys
  import pandas
  from ..arima import AutoARIMA
label_desc: Use image node_id 1 to create a new AutoARIMAProphet object from inherited base classes: Prophet with example: obj = AutoARIMAProphet(growth, changepoints, n_changepoints, changepoint_range, yearly_seasonality, weekly_seasonality, daily_seasonality, holidays, seasonality_mode, seasonality_prior_scale, holidays_prior_scale, changepoint_prior_scale, mcmc_samples, interval_width, uncertainty_samples, stan_backend, d, D, max_p, max_q, max_P, max_Q, max_order, max_d, max_D, start_p, start_q, start_P, start_Q, stationary, seasonal, ic, stepwise, nmodels, trace, approximation, method, truncate, test, test_kwargs, seasonal_test, seasonal_test_kwargs, allowdrift, allowmean, blambda, biasadj, period)