M8 - Model Diagnostics, Selection and Performance

Learning Objectives

The learning goals for this module are:

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

Slides

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

Resources

• Time Series Analysis with Applications - Cryer and Shan - Chapter 8: Diagnostics
  

Recordings

The first video will discuss model selection and performance.

The second video will explore resiual analysis.

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.

• ARIMA Parameter Estimation

• Time Series Analysis with Applications - Cryer and Shan - Chapter 7: Parameter Estimation
  

Deliverables

For this module you will complete Assignment 8. The due date for A8 is March 27.