ETC3550/5550 Applied forecasting

Teaching team

Lecturer/Chief Examiner

Rob J Hyndman

Email: Rob.Hyndman@monash.edu

Head Tutor

Mitchell O’Hara-Wild

Email: Mitch.OHara-Wild@monash.edu

Tutors

• Elena Sanina
• Zhixiang (Elvis) Yang
• Jarryd Chapman
• Xiefei (Sapphire) Li
• Xiaoqian Wang

Weekly schedule

• Pre-recorded videos: approximately 1 hour per week [Slides]
• Tutorials: 1.5 hours in class per week
• Seminars: 9am Fridays, Central 1 Lecture Theatre, 25 Exhibition Walk.

Week	Topic	Chapter	Assessments
26 Feb	Introduction to forecasting and R	1. Getting started
04 Mar	Time series graphics	2. Time series graphics	Assignment 1
11 Mar	Time series decomposition	3. Time series decomposition
18 Mar	The forecaster’s toolbox	5. The forecaster’s toolbox	Assignment 2
25 Mar	Exponential smoothing	8. Exponential smoothing
01 Apr	Mid-semester break
08 Apr	Exponential smoothing	8. Exponential smoothing	Assignment 3
15 Apr	ARIMA models	9. ARIMA models
22 Apr	ARIMA models	9. ARIMA models
29 Apr	ARIMA models	9. ARIMA models	Assignment 4
06 May	Multiple regression and forecasting	7. Time series regression models
13 May	Dynamic regression	10. Dynamic regression models
20 May	Dynamic regression	10. Dynamic regression models	Retail Project

Assessments

• Assignment 1: 2%
• Assignment 2: 6%
• Assignment 3: 6%
• Assignment 4: 6%
• Retail project: 20%
• Final exam: 60%

R package installation

Here is the code to install the R packages we will be using in this unit.

install.packages(c("tidyverse","fpp3", "GGally"), dependencies = TRUE)