docstr: Evaluate on the prediction results and y. We predict horizon time-points ahead the input x
  in fit_eval before evaluation, where the horizon length equals the second dimension size of
  y.
  :param target: target for evaluation.
  :param x: ARIMA predicts the horizon steps foreward from the training data.
      So x should be None as it is not used.
  :param metrics: a list of metrics in string format
  :param rolling: whether to use rolling prediction
  :return: a list of metric evaluation results
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code: def evaluate(self, target, x=None, metrics=["mse"], rolling=False):
      """
      Evaluate on the prediction results and y. We predict horizon time-points ahead the input x
      in fit_eval before evaluation, where the horizon length equals the second dimension size of
      y.
      :param target: target for evaluation.
      :param x: ARIMA predicts the horizon steps foreward from the training data.
          So x should be None as it is not used.
      :param metrics: a list of metrics in string format
      :param rolling: whether to use rolling prediction
      :return: a list of metric evaluation results
      """
      if x is not None:
          raise ValueError("We don't support input x currently")
      if target is None:
          raise ValueError("Input invalid target of None")
      if self.model is None:
          raise Exception(
              "Needs to call fit_eval or restore first before calling evaluate"
          )
  
      forecasts = self.predict(horizon=len(target), rolling=rolling)
  
      return [Evaluator.evaluate(m, target, forecasts) for m in metrics]
  
file_path: analytics-zoo/pyzoo/zoo/chronos/model/arima.py
imports: import os
  import pickle
  from pmdarima.arima import ARIMA
  from pmdarima.arima import ndiffs
  from pmdarima.arima import nsdiffs
  from zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.model.abstract import BaseModel, ModelBuilder
label_desc: Use image node_id 5 for calling the ARIMAModel obj's underlying member method code with example usage: obj.evaluate(target, x, metrics, rolling) and returns: unknown