code: def test_uncertainty_estimation_peyton_manning():
      log.info("testing: Uncertainty Estimation Peyton Manning")
      df = pd.read_csv(PEYTON_FILE)
      playoffs = pd.DataFrame(
          {
              "event": "playoff",
              "ds": pd.to_datetime(
                  [
                      "2008-01-13",
                      "2009-01-03",
                      "2010-01-16",
                      "2010-01-24",
                      "2010-02-07",
                      "2011-01-08",
                      "2013-01-12",
                      "2014-01-12",
                      "2014-01-19",
                      "2014-02-02",
                      "2015-01-11",
                      "2016-01-17",
                      "2016-01-24",
                      "2016-02-07",
                  ]
              ),
          }
      )
      superbowls = pd.DataFrame(
          {
              "event": "superbowl",
              "ds": pd.to_datetime(
                  ["2010-02-07", "2014-02-02", "2016-02-07"]
              ),
          }
      )
      events_df = pd.concat((playoffs, superbowls))
  
      m = NeuralProphet(
          n_forecasts=1,
          loss_func="SmoothL1Loss",
          quantiles=[0.01, 0.99],
          epochs=EPOCHS,
          batch_size=BATCH_SIZE,
          learning_rate=LR,
      )
  
      # add lagged regressors
      if m.n_lags > 0:
          df["A"] = df["y"].rolling(7, min_periods=1).mean()
          df["B"] = df["y"].rolling(30, min_periods=1).mean()
          m = m.add_lagged_regressor(name="A")
          m = m.add_lagged_regressor(name="B", only_last_value=True)
  
      # add events
      m = m.add_events(
          ["superbowl", "playoff"],
          lower_window=-1,
          upper_window=1,
          regularization=0.1,
      )
  
      m = m.add_country_holidays(
          "US", mode="additive", regularization=0.1
      )
  
      df["C"] = df["y"].rolling(7, min_periods=1).mean()
      df["D"] = df["y"].rolling(30, min_periods=1).mean()
  
      m = m.add_future_regressor(name="C", regularization=0.1)
      m = m.add_future_regressor(name="D", regularization=0.1)
  
      history_df = m.create_df_with_events(df, events_df)
  
      m.fit(history_df, freq="D")
  
      periods = 90
      regressors_future_df = pd.DataFrame(
          data={"C": df["C"][:periods], "D": df["D"][:periods]}
      )
      future_df = m.make_future_dataframe(
          df=history_df,
          regressors_df=regressors_future_df,
          events_df=events_df,
          periods=periods,
          n_historic_predictions=360,
      )
      m.predict(df=future_df)
  
file_path: neural_prophet/tests/test_uncertainty.py
imports: import logging
  import os
  import pathlib
  import pandas
  import pytest
  from neuralprophet import NeuralProphet, uncertainty_evaluate
label_desc: Use image node_id 1 for calling a global function with example usage: test_uncertainty_estimation_peyton_manning() without return types