docstr: Predict horizon time-points ahead the input data in fit_eval
  :param data: Prophet predicts the horizon steps foreward from the training data.
      So data should be None as it is not used.
  :param horizon: horizon length to predict
  :param freq: the freqency of the predicted dataframe, defaulted to day("D"),
         the frequency can be anything from the pandas list of frequency strings.
  
  :return: predicted result of length horizon
docstr_tok: ["Predict","horizon","time-points","ahead","the","input","data","in","fit_eval",":","param","data",":","Prophet","predicts","the","horizon","steps","foreward","from","the","training","data",".","So","data","should","be","None","as","it","is","not","used",".",":","param","horizon",":","horizon","length","to","predict",":","param","freq",":","the","freqency","of","the","predicted","dataframe",",","defaulted","to","day","(","``","D","''",")",",","the","frequency","can","be","anything","from","the","pandas","list","of","frequency","strings",".",":","return",":","predicted","result","of","length","horizon"]
code: def predict(self, ds_data=None, horizon=24, freq="D"):
      """
      Predict horizon time-points ahead the input data in fit_eval
      :param data: Prophet predicts the horizon steps foreward from the training data.
          So data should be None as it is not used.
      :param horizon: horizon length to predict
      :param freq: the freqency of the predicted dataframe, defaulted to day("D"),
             the frequency can be anything from the pandas list of frequency strings.
  
      :return: predicted result of length horizon
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if self.model is None:
          invalidInputError(
              False,
              "Needs to call fit_eval or restore first before calling predict",
          )
      if ds_data is not None:
          return self.model.predict(ds_data)
      future = self.model.make_future_dataframe(
          periods=horizon, freq=freq
      )
      out = self.model.predict(future)[-horizon:]
      return out
  
file_path: BigDL/python/chronos/src/bigdl/chronos/model/prophet.py
imports: import json
  import numpy
  from prophet import Prophet
  from prophet.serialize import model_to_json, model_from_json
  from prophet.diagnostics import performance_metrics
  from prophet.diagnostics import cross_validation
  from bigdl.chronos.metric.forecast_metrics import Evaluator
label_desc: Use image node_id 6 for calling the ProphetModel obj's underlying member method code with example usage: obj.predict(ds_data, horizon, freq) and returns: out, self