code: def data(request):
      rs = np.random.RandomState(987654321)
      err = rs.standard_normal(500)
      index = pd.date_range("1980-1-1", freq=MONTH_END, periods=500)
      fourier = Fourier(12, 1)
      terms = fourier.in_sample(index)
      det = np.squeeze(np.asarray(terms @ np.array([[2], [1]])))
      for i in range(1, 500):
          err[i] += 0.9 * err[i - 1] + det[i]
      return pd.Series(err, index=index)
  
imports: from statsmodels.compat.pandas import MONTH_END
  import numpy
  from numpy.testing import assert_allclose
  import pandas
  import pytest
  import statsmodels.datasets
  from statsmodels.tsa.ar_model import AutoReg
  from statsmodels.tsa.arima.model import ARIMA
  from statsmodels.tsa.base.prediction import PredictionResults
  from statsmodels.tsa.deterministic import Fourier
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  from statsmodels.tsa.forecasting.stl import STLForecast
  from statsmodels.tsa.seasonal import STL, DecomposeResult
  from statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing
label_desc: Use image node_id 1 for calling a global function with example usage: data(request) and returns: pd