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AIC and BIC vs ACF and PACF

In this exercise you will apply an AIC-BIC order search for the earthquakes time series. In the last lesson you decided that this dataset looked like an AR(1) process. You will do a grid search over parameters to see if you get the same results. The ACF and PACF plots for this dataset are shown below.

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The ARIMA model class and the time series DataFrame earthquake are available in your environment.

Cet exercice fait partie du cours

ARIMA Models in Python

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Instructions

  • Loop over orders of p and q between 0 and 2.
  • Inside the loop try to fit an ARMA(p,q) to earthquake on each loop.
  • Print p and q alongside AIC and BIC in each loop.
  • If the model fitting procedure fails print p, q, None, None.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Loop over p values from 0-2
for p in ____:
    # Loop over q values from 0-2
    for q in ____:
      
        try:
            # create and fit ARMA(p,q) model
            model = ____
            results = model.____
            
            # Print order and results
            print(p, q, ____, ____)
            
        except:
            print(p, q, ____, ____)
Modifier et exécuter le code