Learning Objectives

The learning goals for this module are:

  • Discuss model selection criteria: Akaike and Bayesian Information Criteria;
  • Discuss residual analyis;
  • Introduce common forecast performance/accuracy metrics;
  • Learn how to compute forecast accuracy in R.


Here is a link to the slide deck used in class.



Optional Readings

If you want to learn more about parameter estimation for the ARIMA model, please refer to the additional material below. The slides will go over how to estimate the autoregressive coefficient (i.e. PACF values), moving average coefficent and variance of residuals.


There is no assignment associated with this module but you will have a chance to explore this content when checking accuracy of the model you develop for the load forecasting cometition and the final project. Visit the Assignments tab to check deadlines for A08 and the Final Project.