class_docstr: Class to test the AutoML flow
      
class_docstr_tok: ["Class","to","test","the","AutoML","flow"]
code: def setUp(self):
      # Pre Release
  
      datapath = "example_datasets/"
      filename1 = "Sales_and_Marketing.csv"
      dft = pd.read_csv(datapath + filename1, index_col=None)
  
      self.ts_column = "Time Period"
      self.sep = ","
      self.target = "Sales"
      self.preds = [
          x for x in list(dft) if x not in [self.ts_column, self.target]
      ]  # Exogenous variable names
  
      self.train_multivar = dft[:40]
      self.test_multivar = dft[40:]
  
      self.train_univar = dft[:40][[self.ts_column, self.target]]
      self.test_univar = dft[40:][[self.ts_column, self.target]]
  
      self.forecast_period = 8
  
      # TODO: Update Prophet column names when you make it consistent
      # self.expected_pred_col_names_prophet = np.array(['mean', 'mean_se', 'mean_ci_lower', 'mean_ci_upper'])
      self.expected_pred_col_names = np.array(
          ["mean", "mean_se", "mean_ci_lower", "mean_ci_upper"]
      )
  
      ################################
      #### Prophet Golden Results ####
      ################################
  
      #########################################################
      #### UNIVARIATE [Both No CV (uses fold2) and CV = 2] ####
      #########################################################
  
      ## Internal (to AutoML) validation set results
      self.forecast_gold_prophet_univar_internal_val_cv_fold1 = np.array(
          [
              # Previous Unbiased Estimate which was giving issues when tested
              # against some time series
              # 447.733878, 510.152411, 536.119254, 599.663702,
              # 592.020981, 657.844486, 667.245238, 485.989273
              # Biased estimate since it does not match other models in terms of
              # validation period or count of observations in each period
              380.824105,
              513.599456,
              509.112615,
              534.484328,
              565.162594,
              633.571696,
              628.649043,
              700.044705,
              713.049146,
              531.755138,
              468.986024,
              431.764757,
              432.869342,
          ]
      )
  
      self.forecast_gold_prophet_univar_internal_val_cv_fold2 = (
          np.array(
              [
                  614.482071,
                  562.050462,
                  534.810663,
                  605.566298,
                  580.899233,
                  585.676464,
                  686.480721,
                  732.167184,
              ]
          )
      )
  
      # Previous Unbiased Estimate which was giving issues when tested
      # against some time series
      # self.rmse_gold_prophet_univar_cv_fold1 = 86.34827037
  
      # Biased estimate since it does not match other models in terms of
      # validation period or count of observations in each period
      self.rmse_gold_prophet_univar_cv_fold1 = 67.12444991
  
      # Previous Unbiased Estimate which was giving issues when tested
      # against some time series
      # self.rmse_gold_prophet_univar_cv_fold2 = 56.5751 # Without CV gets this result
  
      # Biased estimate since it does not match other models in terms of
      # validation period or count of observations in each period
      self.rmse_gold_prophet_univar_cv_fold2 = (
          55.594384  # Without CV gets this result
      )
  
      ## External Test Set results
      results = [
          749.061242,
          751.077262,
          796.892366,
          783.206733,
          689.698130,
          595.713426,
          569.486600,
          635.884371,
      ]
      index = np.arange(40, 48)
  
      self.forecast_gold_prophet_univar_external_test_cv = pd.Series(
          data=results, index=index
      )
      self.forecast_gold_prophet_univar_external_test_cv.name = "yhat"
  
      results = results + [576.473786, 581.275889]
      index = np.arange(40, 50)
  
      self.forecast_gold_prophet_univar_external_test_10_cv = pd.Series(
          data=results, index=index
      )
      self.forecast_gold_prophet_univar_external_test_10_cv.name = (
          "yhat"
      )
  
      ###########################################################
      #### MULTIVARIATE [Both No CV (uses fold2) and CV = 2] ####
      ###########################################################
  
      # Internal (to AutoML) validation set results
      self.forecast_gold_prophet_multivar_internal_val_cv_fold1 = np.array(
          [
              # Previous Unbiased Estimate which was giving issues when tested
              # against some time series
              # 502.111972, 569.181958, 578.128706, 576.069791,
              # 663.258686, 677.851419, 750.972617, 781.269791
              # Biased estimate since it does not match other models in terms of
              # validation period or count of observations in each period
              263.278655,
              515.858752,
              596.916275,
              528.986198,
              500.662700,
              598.963783,
              618.888170,
              689.703134,
              729.021092,
              512.653123,
              482.081110,
              455.731872,
              309.154906,
          ]
      )
  
      self.forecast_gold_prophet_multivar_internal_val_cv_fold2 = np.array(
          [
              # Previous Unbiased Estimate which was giving issues when tested
              # against some time series
              # 618.244315, 555.784628, 524.396122, 611.513751,
              # 584.936717, 605.940656, 702.652641, 736.639273
              # Biased estimate since it does not match other models in terms of
              # validation period or count of observations in each period
              566.442657,
              637.740324,
              636.940975,
              708.781598,
              725.327137,
              542.517575,
              484.142209,
              449.074053,
              505.764104,
              479.320556,
              475.915244,
              570.814914,
              600.585457,
          ]
      )
  
      # Previous Unbiased Estimate which was giving issues when tested
      # against some time series
      # self.rmse_gold_prophet_multivar_cv_fold1 = 48.70419901
  
      # Biased estimate since it does not match other models in terms of
      # validation period or count of observations in each period
      self.rmse_gold_prophet_multivar_cv_fold1 = 116.40578011
  
      # Previous Unbiased Estimate which was giving issues when tested
      # against some time series
      # self.rmse_gold_prophet_multivar_cv_fold2 = 63.24631835 # Without CV gets this result
  
      # Biased estimate since it does not match other models in terms of
      # validation period or count of observations in each period
      self.rmse_gold_prophet_multivar_cv_fold2 = 53.60487935  # Without CV gets this result (for unbiased case, but not for biased case)
  
      ## External Test Set results
      results = [
          747.964093,
          736.512241,
          814.840792,
          825.152970,
          657.743450,
          588.985816,
          556.814528,
          627.768202,
      ]
      index = np.arange(0, 8)
  
      self.forecast_gold_prophet_multivar_external_test_cv = pd.Series(
          data=results, index=index
      )
      self.forecast_gold_prophet_multivar_external_test_cv.name = "yhat"
  
      # Same as regular since we only have 8 exogenous observations
      self.forecast_gold_prophet_multivar_external_test_10_cv = (
          pd.Series(data=results, index=index)
      )
      self.forecast_gold_prophet_multivar_external_test_10_cv.name = (
          "yhat"
      )
  
      # ##############################
      # #### ARIMA Golden Results ####
      # ##############################
  
      # #### UNIVARIATE and MULTIVARIATE ####
  
      # results = [
      #     801.78660584, 743.16044526, 694.38764549, 684.72931967,
      #     686.70229610, 692.13402266, 698.59426282, 705.36034762
      #     ]
      # index = [
      #     'Forecast_1', 'Forecast_2', 'Forecast_3', 'Forecast_4',
      #     'Forecast_5', 'Forecast_6', 'Forecast_7', 'Forecast_8'
      #     ]
  
      # self.forecast_gold_arima_uni_multivar = np.array(results)
  
      # self.forecast_gold_arima_uni_multivar_series = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_arima_uni_multivar_series.name = 'mean'
  
      # results = results + [712.217380, 719.101457]
      # index = index + ['Forecast_9', 'Forecast_10']
  
      # self.forecast_gold_arima_uni_multivar_series_10 = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_arima_uni_multivar_series_10.name = 'mean'
  
      # self.rmse_gold_arima_uni_multivar = 169.00016628
  
      # #######################################################################################################
  
      # ################################
      # #### SARIMAX Golden Results ####
      # ################################
  
      # #### UNIVARIATE ####
  
      # ## Internal (to AutoML) validation set results
      # results = [
      #     803.31673726, 762.46093997, 718.35819310, 711.42130506,
      #     719.36254603, 732.70981867, 747.57645435, 762.47349398
      #     ]
      # self.forecast_gold_sarimax_univar_internal_val = np.array(results)
      # self.rmse_gold_sarimax_univar = 193.49650578
  
      # ## External Test Set results
      # results = [
      #     737.281499, 718.144765, 672.007487, 618.321458,
      #     578.990868, 567.799468, 586.467414, 625.619993
      # ]
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_univar_external_test = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_univar_external_test.name = 'mean'
  
      # results = results + [669.666326, 703.29552]
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01',
      #     '2015-01-01', '2015-02-01'
      #     ])
  
      # self.forecast_gold_sarimax_univar_external_test_10 = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_univar_external_test_10.name = 'mean'
  
      # #######################################
      # #### MULTIVARIATE (no seasonality) ####
      # #######################################
  
      # ## Internal (to AutoML) validation set results
      # results = [
      #     772.268886, 716.337431, 686.167231, 739.269047,
      #     704.280567, 757.450733, 767.711055, 785.960125
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val = np.array(results)
      # self.rmse_gold_sarimax_multivar = 185.704684
  
      # ## External Test Set results (With Multivariate columns accepted)
      # results = [
      #     750.135204, 806.821297, 780.232195, 743.309074,
      #     724.400616, 683.117893, 673.696113, 686.807075
      # ]
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_multivar_external_test = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_multivar_external_test.name = 'mean'
  
      # results = results[0:6]
      # index = index[0:6]
      # self.forecast_gold_sarimax_multivar_external_test_10 = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_multivar_external_test_10.name = 'mean'
  
      # ################################################################################
      # #### MULTIVARIATE (with seasonality = True, Seasonal Period = 12 CV = None) ####
      # ################################################################################
  
      # ## Internal (to AutoML) validation set results (with seasonality = True, Seasonal Period = 12)
      # # Without CV
      # results = [
      #     726.115602, 646.028979, 657.249936, 746.752393,
      #     732.813245, 749.435178, 863.356789, 903.168728
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val_s12 = np.array(results)
      # self.rmse_gold_sarimax_multivar_s12 = 197.18894
  
      # ## External Test Set results
      # # (With Multivariate columns accepted)
      # # (with seasonality = True, Seasonal Period = 12)
  
      # results = [
      #     1006.134134, 779.874076, 420.461804, 724.042104,
      #     1827.304601, 1204.070838, -2216.439611, -1278.974132
      # ]
  
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_multivar_external_test_s12 = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_multivar_external_test_s12.name = 'mean'
  
      # results = results[0:6]
      # index = index[0:6]
      # self.forecast_gold_sarimax_multivar_external_test_10_s12 = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_multivar_external_test_10_s12.name = 'mean'
  
      # #############################################################################
      # #### MULTIVARIATE (with seasonality = True, Seasonal Period = 3, CV = 2) ####
      # #############################################################################
  
      # ## Internal (to AutoML) validation set results
  
      # results = [
      #     119.260686, 540.623654, 230.040446, 364.088969,
      #     470.581971, 105.559723, 84.335069, 110.757574
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val_s3_cv_fold1 = np.array(results)
  
      # results = [
      #     551.736392, 502.232401, 440.047123, 521.382176,
      #     496.012325, 501.563083, 634.825011, 674.975611
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val_s3_cv_fold2 = np.array(results)
  
      # self.rmse_gold_sarimax_multivar_s12_cv = 239.191102
      # self.rmse_gold_sarimax_multivar_s3_cv_fold1 = 443.839435
      # self.rmse_gold_sarimax_multivar_s3_cv_fold2 = 34.542769
  
      # ## External Test Set results
      # results = [
      #     770.447134, 784.881945, 857.496478, 918.626627,
      #     689.107408, 599.827292, 608.747367, 634.957579
      # ]
  
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_multivar_external_test_s3_cv = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_multivar_external_test_s3_cv.name = 'mean'
  
      # results = results[0:6]
      # index = index[0:6]
      # self.forecast_gold_sarimax_multivar_external_test_10_s3_cv = pd.Series(
      #     data=results,
      #     index=index
      # )
      # self.forecast_gold_sarimax_multivar_external_test_10_s3_cv.name = 'mean'
  
      #######################################################################################################
  
      #####################################
      #### Auto SARIMAX Golden Results ####
      #####################################
  
      #### UNIVARIATE ####
  
      ## Internal (to AutoML) validation set results
      # results = [
      #     803.31673726, 762.46093997, 718.3581931,  711.42130506,
      #     719.36254603, 732.70981867, 747.57645435, 762.47349398
      #     ]
      # self.forecast_gold_sarimax_univar_internal_val = np.array(results)
      self.rmse_gold_auto_sarimax_univar = 128.034697
  
      ## External Test Set results
      # results=[
      #     737.281499, 718.144765, 672.007487, 618.321458,
      #     578.990868, 567.799468, 586.467414, 625.619993
      # ]
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_univar_external_test = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_univar_external_test.name = 'mean'
  
      # results = results + [669.666326, 703.29552]
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01',
      #     '2015-01-01', '2015-02-01'
      #     ])
  
      # self.forecast_gold_sarimax_univar_external_test_10 = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_univar_external_test_10.name = 'mean'
  
      #######################################
      #### MULTIVARIATE (no seasonality) ####
      #######################################
  
      # ## Internal (to AutoML) validation set results
      # results = [
      #     772.268886, 716.337431, 686.167231, 739.269047,
      #     704.280567, 757.450733, 767.711055, 785.960125
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val = np.array(results)
      self.rmse_gold_auto_sarimax_multivar = 147.703077
  
      ## External Test Set results (With Multivariate columns accepted)
      # results = [
      #     750.135204, 806.821297, 780.232195, 743.309074,
      #     724.400616, 683.117893, 673.696113, 686.807075
      # ]
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_multivar_external_test = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_multivar_external_test.name = 'mean'
  
      # results = results[0:6]
      # index = index[0:6]
      # self.forecast_gold_sarimax_multivar_external_test_10 = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_multivar_external_test_10.name = 'mean'
  
      ################################################################################
      #### MULTIVARIATE (with seasonality = True, Seasonal Period = 12 CV = None) ####
      ################################################################################
  
      ## Internal (to AutoML) validation set results (with seasonality = True, Seasonal Period = 12)
      # # Without CV
      # results = [
      #     726.115602, 646.028979, 657.249936, 746.752393,
      #     732.813245, 749.435178, 863.356789, 903.168728
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val_s12 = np.array(results)
      self.rmse_gold_auto_sarimax_multivar_s12 = 90.083682
  
      ## External Test Set results (With Multivariate columns accepted)
      # (with seasonality = True, Seasonal Period = 12)
  
      # results = [
      #     1006.134134, 779.874076, 420.461804, 724.042104,
      #     1827.304601, 1204.070838, -2216.439611, -1278.974132
      # ]
  
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_multivar_external_test_s12 = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_multivar_external_test_s12.name = 'mean'
  
      # results = results[0:6]
      # index = index[0:6]
      # self.forecast_gold_sarimax_multivar_external_test_10_s12 = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_multivar_external_test_10_s12.name = 'mean'
  
      #############################################################################
      #### MULTIVARIATE (with seasonality = True, Seasonal Period = 3, CV = 2) ####
      #############################################################################
  
      ## Internal (to AutoML) validation set results
  
      # results = [
      #     119.260686, 540.623654, 230.040446, 364.088969,
      #     470.581971, 105.559723, 84.335069, 110.757574
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val_s3_cv_fold1 = np.array(results)
  
      # results = [
      #     551.736392, 502.232401, 440.047123, 521.382176,
      #     496.012325, 501.563083, 634.825011, 674.975611
      # ]
      # self.forecast_gold_sarimax_multivar_internal_val_s3_cv_fold2 = np.array(results)
  
      self.rmse_gold_auto_sarimax_multivar_s12_cv = 268.191766
      # self.rmse_gold_auto_sarimax_multivar_s3_cv_fold1 = 443.839435
      # self.rmse_gold_auto_sarimax_multivar_s3_cv_fold2 = 34.542769
  
      # ## External Test Set results
      # results = [
      #     770.447134, 784.881945, 857.496478, 918.626627,
      #     689.107408, 599.827292, 608.747367, 634.957579
      # ]
  
      # index = pd.to_datetime([
      #     '2014-05-01', '2014-06-01', '2014-07-01', '2014-08-01',
      #     '2014-09-01', '2014-10-01', '2014-11-01', '2014-12-01'
      #     ])
  
      # self.forecast_gold_sarimax_multivar_external_test_s3_cv = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_multivar_external_test_s3_cv.name = 'mean'
  
      # results = results[0:6]
      # index = index[0:6]
      # self.forecast_gold_sarimax_multivar_external_test_10_s3_cv = pd.Series(
      #         data = results,
      #         index = index
      #     )
      # self.forecast_gold_sarimax_multivar_external_test_10_s3_cv.name = 'mean'
  
      #######################################################################################################
  
      ############################
      #### VAR Golden Results ####
      ############################
      self.rmse_gold_var_multivar = 112.4770318
  
      ###########################
      #### ML Golden Results ####
      ###########################
  
      #### UNIVARIATE ####
      self.forecast_gold_ml_univar = None
      self.rmse_gold_ml_univar = math.inf
      self.forecast_gold_ml_univar_series = None
      self.forecast_gold_ml_univar_series_10 = None
  
      ##################################
      #### MULTIVARIATE (CV = None) ####
      ##################################
  
      ## Internal (to AutoML) validation set results
      # Initially only this was using rolling window
      # results = [
      #     509.64, 447.34, 438.2 , 456.98,
      #     453.04, 449.36, 530.02, 626.8
      # ]
      # Converted to using same as other model (no rolling window), but unable to return since
      # no internal test set (only validation sets in CV)
      results = []
      self.forecast_gold_ml_multivar_internal_val = (
          results  # np.array(results)
      )
      # self.rmse_gold_ml_multivar = 74.133644 # Initially only this was using rolling window
      # self.rmse_gold_ml_multivar = 67.304009 # Converted to using same as other model (no rolling window)
      self.rmse_gold_ml_multivar = (
          71.824585  # With more engineered features (AutoViML)
      )
  
      ## External Test Set results (With Multivariate columns accepted)
      # # Initially only this was using rolling window
      # results = [
      #     509.64, 485.24, 479.72, 483.98,
      #     482.78, 455.04, 518.62, 524.08
      # ]
  
      # Converted to using same as other model (no rolling window)
      results = [
          # 677.0, 652.85, 652.85, 652.85,
          # 640.458333, 505.043478, 494.571429, 494.571429
          # With more engineered features (AutoViML)
          # 733.293931, 627.633457, 621.182141, 614.128809,
          # 600.902623, 451.565462, 330.694427, 348.744604
          715.997991,
          592.578743,
          569.592338,
          547.625097,
          513.972428,
          334.603163,
          188.705675,
          180.323713,
      ]
      # index = pd.RangeIndex(start=40, stop=48, step=1)
      # index = pd.to_datetime([
      index = [
          "2014-05-01",
          "2014-06-01",
          "2014-07-01",
          "2014-08-01",
          "2014-09-01",
          "2014-10-01",
          "2014-11-01",
          "2014-12-01",
      ]
  
      self.forecast_gold_ml_multivar_external_test = pd.Series(
          data=results, index=index
      )
      self.forecast_gold_ml_multivar_external_test.name = "mean"
      self.forecast_gold_ml_multivar_external_test.index.name = (
          self.ts_column
      )
  
      results = results[0:6]
      index = index[0:6]
      # self.forecast_gold_ml_multivar_external_test_10_cv = pd.Series(
      self.forecast_gold_ml_multivar_external_test_10 = pd.Series(
          data=results, index=index
      )
      # self.forecast_gold_ml_multivar_external_test_10_cv.name = 'mean'
      self.forecast_gold_ml_multivar_external_test_10.name = "mean"
      self.forecast_gold_ml_multivar_external_test_10.index.name = (
          self.ts_column
      )
  
      ###############################
      #### MULTIVARIATE (CV = 2) ####
      ###############################
  
      # self.rmse_gold_ml_multivar_cv = 80.167235
      # self.rmse_gold_ml_multivar_cv_fold1 = 93.03046227
      # self.rmse_gold_ml_multivar_cv_fold2 = 67.30400862
  
      # With more engineered features (AutoViML)
      self.rmse_gold_ml_multivar_cv = 94.472165
      self.rmse_gold_ml_multivar_cv_fold1 = 107.20370095
      self.rmse_gold_ml_multivar_cv_fold2 = 81.740629
  
      results = [
          # 677.000000, 652.850000, 652.850000, 652.850000,
          # 640.458333, 505.043478, 494.571429, 494.571429
          # With more engineered features (AutoViML)
          # 652.85, 640.458333, 640.458333, 640.458333,
          # 559.583333, 494.571429, 494.571429, 494.571429
          652.85,
          645.0,
          645.0,
          645.0,
          640.458333,
          505.043478,
          485.444444,
          485.444444,
      ]
      # index = pd.RangeIndex(start=40, stop=48, step=1)
      # index = pd.to_datetime([
      index = [
          "2014-05-01",
          "2014-06-01",
          "2014-07-01",
          "2014-08-01",
          "2014-09-01",
          "2014-10-01",
          "2014-11-01",
          "2014-12-01",
      ]
  
      self.forecast_gold_ml_multivar_external_test_cv = pd.Series(
          data=results, index=index
      )
      self.forecast_gold_ml_multivar_external_test_cv.name = "mean"
      self.forecast_gold_ml_multivar_external_test_cv.index.name = (
          self.ts_column
      )
  
      results = results[0:6]
      index = index[0:6]
      # self.forecast_gold_ml_multivar_external_test_10 = pd.Series(
      self.forecast_gold_ml_multivar_external_test_10_cv = pd.Series(
          data=results, index=index
      )
      # self.forecast_gold_ml_multivar_external_test_10.name = 'mean'
      self.forecast_gold_ml_multivar_external_test_10_cv.name = "mean"
      self.forecast_gold_ml_multivar_external_test_10_cv.index.name = (
          self.ts_column
      )
  
imports: import sys
  import os
  import unittest
  import math
  import numpy
  import pandas
  from pandas.testing import assert_series_equal
  from pandas.testing import assert_frame_equal
  from prophet.forecaster import Prophet
  from statsmodels.tsa.statespace.sarimax import SARIMAXResultsWrapper
  from sklearn.base import BaseEstimator
  from auto_ts import auto_timeseries
label_desc: Use image node_id 1 for calling the TestAutoTS obj's underlying member method code with example usage: obj.setUp() without return types