lots_of_SARIMA

fig, axs = plt.subplots(4, 5, sharex=True)

fig.set_size_inches(16,12)

x = y = 0

for issue in time_series:
  train_l = 55

  s_model = SARIMAX(endog = time_series[[issue]][:train_l][1:],
                    exog = time_series[[x for x in time_series.columns if x != issue]][:train_l].shift().add_suffix("_l1")[1:],
                    order=(2,0,1), seasonal_order=(1,0,1,7)).fit()


  f_ru = time_series[[issue]].copy()[1:]
  f_ru.columns = ["actual"]
  f_ru["predicted"] = s_model.predict(end=datetime.datetime(2019, 10, 6), endog = time_series[[issue]][-5:],exog = time_series[[x for x in time_series.columns if x != issue]].shift()[-5:], 
                                            dynamic= False)
  
  testing = f_ru[-5:].copy()

  f_ru["actual"].plot(title="{}\nRMSE {}".format(issue, sqrt(mean_squared_error(testing.actual, testing.predicted))), ax=axs[x,y])
  f_ru["predicted"][:-5].plot(color="orange", label="predicted: Train", ax=axs[x,y])
  f_ru["predicted"][-6:].plot(color="red", label="predicted: Test", ax=axs[x,y])

  x+=1
  if x > 3:
    x =0
    y+=1

handles, labels = axs[0,0].get_legend_handles_labels()
fig.legend(handles, labels, loc='center right')