class_docstr: Example:
      >>> #The dataset is split into data, validation_data
      >>> model = ARIMAForecaster(p=2, q=2, seasonality_mode=False)
      >>> model.fit(data, validation_data)
      >>> predict_result = model.predict(horizon=24)
class_docstr_tok: ["Example",":",">",">",">","#","The","dataset","is","split","into","data",",","validation_data",">",">",">","model","=","ARIMAForecaster","(","p=2",",","q=2",",","seasonality_mode=False",")",">",">",">","model.fit","(","data",",","validation_data",")",">",">",">","predict_result","=","model.predict","(","horizon=24",")"]
docstr: Predict using a trained forecaster.
  
  :param horizon: the number of steps forward to predict
  :param rolling: whether to use rolling prediction
  
  :return: A list in length of horizon reflects the predict result.
docstr_tok: ["Predict","using","a","trained","forecaster",".",":","param","horizon",":","the","number","of","steps","forward","to","predict",":","param","rolling",":","whether","to","use","rolling","prediction",":","return",":","A","list","in","length","of","horizon","reflects","the","predict","result","."]
code: def predict(self, horizon, rolling=False):
      """
      Predict using a trained forecaster.
  
      :param horizon: the number of steps forward to predict
      :param rolling: whether to use rolling prediction
  
      :return: A list in length of horizon reflects the predict result.
      """
      if self.internal.model is None:
          from bigdl.nano.utils.common import invalidInputError
  
          invalidInputError(
              False,
              "You must call fit or restore first before calling predict!",
          )
      return self.internal.predict(horizon=horizon, rolling=rolling)
  
file_path: BigDL/python/chronos/src/bigdl/chronos/forecaster/arima_forecaster.py
imports: from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.model.arima import ARIMAModel
label_desc: Use image node_id 4 for calling the ARIMAForecaster obj's underlying member method code with example usage: obj.predict(horizon, rolling) and returns: self