Tutorial learning objectives - Week 10

By the end of this tutorial, you should be able to:

1. Understand and perform data transformation as needed.

2. Understand the concept of time series stationarity and implement appropriate differencing when necessary.

3. Able to Identify and compare non-seasonal/ seasonal ARIMA models using ACF/PACF diagnostics and information criteria such as AIC.

4. Estimate the parameters of an ARIMA model, conduct residual diagnostics, and assess the adequacy of model fit using white noise tests.

5. Able to produce forecasts for the estimated ARIMA model.