Answer Happy

pleaaaaaaaaaase Don't copy previous solution
!!!!!!
EXPLAIN EACH LINE
rolling_mean = df_log.rolling(window=12).mean()
df_log_minus_mean = df_log - rolling_mean
df_log_minus_mean.dropna(inplace=True)
get_stationarity(df_log_minus_mean)
df_log_shift = df_log - df_log.shift()
df_log_shift.dropna(inplace=True)
get_stationarity(df_log_shift)
decomposition = seasonal_decompose(df_log)
model = ARIMA(df_log, order=(2,1,2))
results = model.fit(disp=-1)
plt.plot(df_log_shift)
plt.plot(results.fittedvalues, color='red')
predictions_ARIMA_diff = pd.Series(results.fittedvalues,
copy=True)
predictions_ARIMA_diff_cumsum =
predictions_ARIMA_diff.cumsum()
predictions_ARIMA_log = pd.Series(df_log['#Passengers'].iloc[0],
index=df_log.index)
predictions_ARIMA_log =
predictions_ARIMA_log.add(predictions_ARIMA_diff_cumsum,
fill_value=0)
predictions_ARIMA = np.exp(predictions_ARIMA_log)
plt.plot(df)
plt.plot(predictions_ARIMA)
results.plot_predict(1,264)