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 or callable function with format
         it signature should be func(y_true, y_pred), where y_true and y_pred are numpy
         ndarray. The function should return a float value as evaluation result.
  :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 or callable function with format
             it signature should be func(y_true, y_pred), where y_true and y_pred are numpy
             ndarray. The function should return a float value as evaluation result.
      :param rolling: whether to use rolling prediction
      :return: a list of metric evaluation results
      """
      from bigdl.nano.utils.common import invalidInputError
  
      if x is not None:
          invalidInputError(False, "We don't support input x currently")
      if target is None:
          invalidInputError(False, "Input invalid target of None")
      if self.model is None:
          invalidInputError(
              False,
              "Needs to call fit_eval or restore first before calling evaluate",
          )
  
      forecasts = self.predict(horizon=len(target), rolling=rolling)
  
      return Evaluator.evaluate(
          metrics, target, forecasts, aggregate="mean"
      )
  
file_path: BigDL/python/chronos/src/bigdl/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 bigdl.chronos.metric.forecast_metrics import Evaluator
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: Evaluator