Identification II
You learned that the savings
time series is stationary without differencing. Now that you have this information you can try and identify what order of model will be the best fit.
The plot_acf()
and the plot_pacf()
functions have been imported and the time series has been loaded into the DataFrame savings
.
Este exercício faz parte do curso
ARIMA Models in Python
Instruções do exercício
- Make a plot of the ACF, for lags 1-10 and plot it on axis
ax1
. - Do the same for the PACF.
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# Create figure
fig, (ax1, ax2) = plt.subplots(2,1, figsize=(12,8))
# Plot the ACF of savings on ax1
____
# Plot the PACF of savings on ax2
____
plt.show()