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  3. ARIMA Models in Python

Exercise

Choosing SARIMA order

In this exercise you will find the appropriate model order for a new set of time series. This is monthly series of the number of employed persons in Australia (in thousands). The seasonal period of this time series is 12 months.

You will create non-seasonal and seasonal ACF and PACF plots and use the table below to choose the appropriate model orders.

AR(p) MA(q) ARMA(p,q)
ACF Tails off Cuts off after lag q Tails off
PACF Cuts off after lag p Tails off Tails off

The DataFrame aus_employment and the functions plot_acf() and plot_pacf() are available in your environment.

Note that you can take multiple differences of a DataFrame using df.diff(n1).diff(n2).

Instructions 1/4

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  • Take the first order difference and the seasonal difference of the aus_employment and drop the NaN values. The seasonal period is 12 months.