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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 ejercicio forma parte del curso

ARIMA Models in Python

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Instrucciones del ejercicio

  • Make a plot of the ACF, for lags 1-10 and plot it on axis ax1.
  • Do the same for the PACF.

Ejercicio interactivo práctico

Prueba este ejercicio y completa el código de muestra.

# 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()
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