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Diagnostic summary statistics

It is important to know when you need to go back to the drawing board in model design. In this exercise you will use the residual test statistics in the results summary to decide whether a model is a good fit to a time series.

Here is a reminder of the tests in the model summary:

Test Null hypothesis P-value name
Ljung-Box There are no correlations in the residual
Prob(Q)
Jarque-Bera The residuals are normally distributed Prob(JB)

An unknown time series df and the ARIMA model class are available for you in your environment.

This exercise is part of the course

ARIMA Models in Python

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create and fit model
model1 = ARIMA(df, order=____)
results1 = model1.fit()

# Print summary
print(____)
Edit and Run Code