mape_after_google.py

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

fig.set_size_inches(16,12)

x = y = 0

for issue in time_series:
  if not issue.find(".com") > -1:
    continue
  train_l = len(time_series)-5

  selected_series = time_series[[col for col in time_series.columns if (col.find(issue[issue.find("_")+1:]) > -1)]]
  pub_series = time_series[[col for col in time_series.columns if (col.find(issue[:issue.find("_")]) > -1)]].drop(columns=issue)
  selected_series = selected_series.join(pub_series)

  s_model = SARIMAX(endog = selected_series[[issue]][:train_l][1:],
                    exog = selected_series[[x for x in selected_series.columns if x != issue]][:train_l].shift().add_suffix("_l1")[1:],
                    order=(3,1,1), seasonal_order=(1,0,1,7)).fit()


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

  testing["error"] = np.abs((testing["actual"] - testing["predicted"]) / testing["actual"])
  fit = round(testing[testing["actual"] != 0].error.mean()*100)
  
  mape_df.loc[issue, "WithSearch_model"] = fit

  testing2 = testing[-5:]
  fit_p = round(testing2[testing2["actual"] != 0].error.mean()*100)
  mape_df.loc[issue, "WithSearch_predicted"] = fit_p



  f_ru["actual"].plot(title="{}\nMAPE: test: {}% model: {}%".format(issue, fit_p, fit), ax=axs[x,y], ylim=(0,100))
  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')

fig.suptitle("Model and Predicted News Coverage including Search", y=1)