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.
This exercise is part of the course
ARIMA Models in Python
Exercise instructions
- Make a plot of the ACF, for lags 1-10 and plot it on axis
ax1. - Do the same for the PACF.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# 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()