SARIMA_model_def

from statsmodels.tsa.statespace.sarimax import SARIMAX

def gen_SARIMA_result(p,d,q, df, issue, test_length): #takes pdq, data, issue to use

  s_model = SARIMAX(endog = df[issue][:-test_length],
                    exog = df[[x for x in df.columns if x != issue]][:-test_length],
                    order=(p,d,q), seasonal_order=(1,0,1,7)).fit()

  f_ru = df[[issue]].copy()[1:] #haven't bothered to change this, but it's the results

  f_ru.columns = ["actual"] #renames the actual values actual
  f_ru["predicted"] = s_model.predict(end=time_series.index.max(), endog = df[[issue]][-test_length:],exog = df[[x for x in selected_series.columns if x != issue]][-test_length:], 
                                            dynamic= False) #predicts future values (1 step)

  f_ru["error"] = np.abs((f_ru["actual"] - f_ru["predicted"]) / f_ru["actual"]) #labels each with the % error

  return f_ru, s_model.aic