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: Evaluate using a trained forecaster.
  
  :param validation_data: A 1-D numpy array as the evaluation data
  :param metrics: A list contains metrics for test/valid data.
  
  :return: A list in length of len(metrics), where states the metrics in order.
docstr_tok: ["Evaluate","using","a","trained","forecaster",".",":","param","validation_data",":","A","1-D","numpy","array","as","the","evaluation","data",":","param","metrics",":","A","list","contains","metrics","for","test\/valid","data",".",":","return",":","A","list","in","length","of","len","(","metrics",")",",","where","states","the","metrics","in","order","."]
code: def evaluate(self, validation_data, metrics=["mse"], rolling=False):
      """
      Evaluate using a trained forecaster.
  
      :param validation_data: A 1-D numpy array as the evaluation data
      :param metrics: A list contains metrics for test/valid data.
  
      :return: A list in length of len(metrics), where states the metrics in order.
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if validation_data is None:
          invalidInputError(
              False, "Input invalid validation_data of None"
          )
      if self.internal.model is None:
          invalidInputError(
              False,
              "You must call fit or restore first before calling evaluate!",
          )
      return self.internal.evaluate(
          validation_data, metrics=metrics, 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 5 for calling the ARIMAForecaster obj's underlying member method code with example usage: obj.evaluate(validation_data, metrics, rolling) and returns: self