class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ProphetForecaster(changepoint_prior_scale=0.05, seasonality_mode='additive')
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
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docstr: Build a Prophet Forecast Model.
  User can customize changepoint_prior_scale, seasonality_prior_scale,
  holidays_prior_scale, seasonality_mode, changepoint_range and metric
  of the Prophet model, for details of the Prophet model hyperparameters, refer to
  https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.
  
  :param changepoint_prior_scale: hyperparameter changepoint_prior_scale for the
      Prophet model.
  :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
      Prophet model.
  :param holidays_prior_scale: hyperparameter holidays_prior_scale for the
      Prophet model.
  :param seasonality_mode: hyperparameter seasonality_mode for the
      Prophet model.
  :param changepoint_range: hyperparameter changepoint_range for the
      Prophet model.
  :param metric: the metric for validation and evaluation. For regression, we support
      Mean Squared Error: ("mean_squared_error", "MSE" or "mse"),
      Mean Absolute Error: ("mean_absolute_error","MAE" or "mae"),
      Mean Absolute Percentage Error: ("mean_absolute_percentage_error", "MAPE", "mape")
      Cosine Proximity: ("cosine_proximity", "cosine")
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code: def __init__(
      self,
      changepoint_prior_scale=0.05,
      seasonality_prior_scale=10.0,
      holidays_prior_scale=10.0,
      seasonality_mode="additive",
      changepoint_range=0.8,
      metric="mse",
  ):
      """
      Build a Prophet Forecast Model.
      User can customize changepoint_prior_scale, seasonality_prior_scale,
      holidays_prior_scale, seasonality_mode, changepoint_range and metric
      of the Prophet model, for details of the Prophet model hyperparameters, refer to
      https://facebook.github.io/prophet/docs/diagnostics.html#hyperparameter-tuning.
  
      :param changepoint_prior_scale: hyperparameter changepoint_prior_scale for the
          Prophet model.
      :param seasonality_prior_scale: hyperparameter seasonality_prior_scale for the
          Prophet model.
      :param holidays_prior_scale: hyperparameter holidays_prior_scale for the
          Prophet model.
      :param seasonality_mode: hyperparameter seasonality_mode for the
          Prophet model.
      :param changepoint_range: hyperparameter changepoint_range for the
          Prophet model.
      :param metric: the metric for validation and evaluation. For regression, we support
          Mean Squared Error: ("mean_squared_error", "MSE" or "mse"),
          Mean Absolute Error: ("mean_absolute_error","MAE" or "mae"),
          Mean Absolute Percentage Error: ("mean_absolute_percentage_error", "MAPE", "mape")
          Cosine Proximity: ("cosine_proximity", "cosine")
      """
      self.model_config = {
          "changepoint_prior_scale": changepoint_prior_scale,
          "seasonality_prior_scale": seasonality_prior_scale,
          "holidays_prior_scale": holidays_prior_scale,
          "seasonality_mode": seasonality_mode,
          "changepoint_range": changepoint_range,
          "metric": metric,
      }
      self.internal = ProphetModel()
  
      super().__init__()
  
file_path: BigDL/python/chronos/src/bigdl/chronos/forecaster/prophet_forecaster.py
imports: from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.model.prophet import ProphetModel
label_desc: Use image node_id 1 to create a new ProphetForecaster object from inherited base classes: Forecaster with example: obj = ProphetForecaster(changepoint_prior_scale, seasonality_prior_scale, holidays_prior_scale, seasonality_mode, changepoint_range, metric)