Tutorial learning objectives - Week 12

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

1. Identify when ARIMA error structures are appropriate in time series regression and explain how they improve forecast accuracy.

2. Apply dynamic harmonic regression using Fourier terms to model complex seasonal patterns in time series data.

3. Integrate Fourier terms with ARIMA errors in a regression framework to capture both seasonal effects and autocorrelation, and evaluate the resulting model performance.