text: import argparse
  import pandas as pd
  import matplotlib.pyplot as pyplot
  from pmdarima.arima import auto_arima
  
  
  def load_csv(csv_name):
      df = pd.read_csv(csv_name, parse_dates=['date'])
      data_train = df[['date','power']]
      data_train = data_train.set_index('date')
  
      return data_train
  
  
  def arima(data_train):
      arima_model =  auto_arima(data_train, start_p=2, d=None, start_q=2,
                                max_p=5, max_d=2, max_q=5, start_P=1, 
                                D=None, start_Q=1, max_P=2, max_D=1, 
                                max_Q=2, max_order=5, m=1, seasonal=False, 
                                stationary=True, information_criterion='aic', 
                                alpha=0.05, test='kpss', seasonal_test='ocsb', 
                                stepwise=True, n_jobs=1, start_params=None, trend=None, 
                                method='lbfgs', maxiter=50, offset_test_args=None, 
                                seasonal_test_args=None, suppress_warnings=True, 
                                error_action='trace', trace=False, random=False, 
                                random_state=None, n_fits=10, out_of_sample_size=0, 
                                scoring='mse', scoring_args=None, with_intercept='auto')
      # predict
      data_predict = arima_model.predict(n_periods=7)
      date = {'date' : ['20210323', '20210324', '20210325', '20210326', '20210327', '20210328', '20210329'],
              'operating_reserve(MW)' : data_predict}
      prediction = pd.DataFrame(date)
  
      return prediction
  
  
  if __name__ == '__main__':
  
      parser = argparse.ArgumentParser()
      parser.add_argument('--training', 
                          default='20200101_20210318.csv',
                          help= 'input training data file name')
      parser.add_argument('--output',
                          default='submission.csv',
                          help='output file name')
      args = parser.parse_args()
      
      # load data
      data_train = load_csv(args.training)
      # predict
      data_predict = arima(data_train)
      # save date
      data_predict.to_csv(args.output, index=0)