code: def test_compare_arimas():
      evaluator = Evaluator(quantiles=[0.1, 0.5, 0.9])
  
      arima = RForecastPredictor(
          freq=freq,
          prediction_length=24 * 7,
          period=period,
          params={
              "quantiles": [0.50, 0.10, 0.90],
              "output_types": ["mean", "quantiles"],
          },
          method_name="arima",
      )
  
      arima_eval_metrics, _ = backtest_metrics(
          test_dataset=dataset, predictor=arima, evaluator=evaluator
      )
  
      fourier_arima = RForecastPredictor(
          freq=freq,
          prediction_length=24 * 7,
          period=period,
          params={
              "quantiles": [0.50, 0.10, 0.90],
              "output_types": ["mean", "quantiles"],
          },
          method_name="fourier.arima",
      )
      fourier_arima_eval_metrics, _ = backtest_metrics(
          test_dataset=dataset,
          predictor=fourier_arima,
          evaluator=evaluator,
      )
  
      assert (
          fourier_arima_eval_metrics["MASE"]
          < arima_eval_metrics["MASE"]
      )
  
file_path: gluonts/test/ext/r_forecast/test_r_multi_seasonality.py
imports: from typing import List
  import numpy
  import pandas
  import pytest
  from gluonts.evaluation import Evaluator, backtest_metrics
  from gluonts.ext.r_forecast import R_IS_INSTALLED, RPY2_IS_INSTALLED, RForecastPredictor
  from gluonts.model.forecast import QuantileForecast
label_desc: Use image node_id 3 for calling a global function with example usage: test_compare_arimas() without return types