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.
Questo esercizio fa parte del corso
ARIMA Models in Python
Istruzioni dell'esercizio
- Make a plot of the ACF, for lags 1-10 and plot it on axis
ax1. - Do the same for the PACF.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Create figure
fig, (ax1, ax2) = plt.subplots(2,1, figsize=(12,8))
# Plot the ACF of savings on ax1
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# Plot the PACF of savings on ax2
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plt.show()