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  3. ARIMA Models in Python

Exercise

Plot diagnostics

It is important to know when you need to go back to the drawing board in model design. In this exercise you will use 4 common plots to decide whether a model is a good fit to some data.

Here is a reminder of what you would like to see in each of the plots for a model that fits well:

Test Good fit
Standardized residual There are no obvious patterns in the residuals
Histogram plus kde estimate The KDE curve should be very similar to the normal distribution
Normal Q-Q Most of the data points should lie on the straight line
Correlogram 95% of correlations for lag greater than zero should not be significant

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

Instructions 1/3

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  • Fit an ARIMA(1,1,1) model to the time series df.
  • Create the 4 diagnostic plots.