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.
Deze oefening maakt deel uit van de cursus
ARIMA Models in Python
Oefeninstructies
- Make a plot of the ACF, for lags 1-10 and plot it on axis
ax1. - Do the same for the PACF.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
# 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()