M4 - Missing Data and Outliers

Learning Objectives

The learning goals for this module are:

• Learn how to handle missing data in time series analysis;
  
• Discuss the different types of outliers and causes;
  
• Learn how to identify and properly remove outliers from a time series;
  
• Get familiar with R functions to handle missing data and outliers (package outliers).
  

Slides

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

Resources

Additional information and resources for this module will be found below.

• Package Outliers Documentation

• Forecasting: Principles and Practice by Rob J Hyndman and George Athanasopoulos, Section 12.9: Dealing with missing values and outliers

Deliverables

For this module you will complete Assignment 5. The due date for A5 is February 19th.