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etc3550_intro.Rmd
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---
title: "ETC3550: Applied forecasting for business and economics"
author: " "
date: " "
fontsize: 14pt
output:
beamer_presentation:
fig_width: 8
fig_height: 5
highlight: tango
theme: metropolis
includes:
in_header: header.tex
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE, cache=TRUE)
library(fpp2)
library(ggplot2)
```
## Contact details
### Lecturer
**Professor Rob Hyndman**
- Room E762, Menzies Building
- Email: [email protected]
### Tutor
**Puwasala Gamakumara**
- Email: [email protected]
## Brief bio
- Professor of Statistics, Monash University
- Editor-in-Chief, *International Journal of Forecasting*
### How my forecasting methodology is used:
- Pharmaceutical Benefits Scheme
- Cancer incidence and mortality
- Electricity demand
- Ageing population
- Fertilizer sales
\begin{textblock}{5.9}(6.6,8.2)\begin{alertblock}{}\Large robjhyndman.com\end{alertblock}\end{textblock}
## Unit objectives
1. To obtain an understanding of common statistical methods used in business and economic forecasting.
2. To develop the computer skills required to forecast business and economic time series data;
3. To gain insights into the problems of implementing and operating large scale forecasting systems for use in business.
### Teaching and learning approach
Two 50 minute classes and a one 80 minute computer lab session each week for 12 weeks.
## R
\placefig{.4}{1.2}{width=6cm}{Rlogo}
\placefig{7}{3.5}{width=5.5cm}{RStudio-Ball}
\begin{textblock}{5.5}(7,3)\centerline{\structure{RStudio}}\end{textblock}
## Key reference
\begin{block}{}\bf
\hangafter=1\hangindent=.3cm
{Hyndman, R.~J. \& Athanasopoulos, G. (2018) \emph{Forecasting: principles and practice}, 2nd edition}
\end{block}\pause
\begin{alertblock}{}\Large
\centerline{\bf OTexts.org/fpp2/}
\end{alertblock}
\pause
* Free and online
* Data sets in associated R package `fpp2`
* R code for examples
### Install required packages
```
install.packages("fpp2", dependencies=TRUE)
```
## Outline
\hbox{\hspace*{-0.5cm}\begin{tabular}{rp{8cm}l}
\toprule
\textbf{Week} & \textbf{Topic} & \textbf{Chapter} \\
\midrule
1 & Introduction to forecasting and R & 1 \\
2 & Introduction to forecasting and R & 2\\
3 & Time series graphics \& decomposition & 3,6\\
4--5 & Exponential smoothing & 7 \\
6--8 & Forecasting with ARIMA models & 8 \\
9--10 & Multiple regression and forecasting & 5 \\
11 & Dynamic regression & 9 \\
12 & Advanced methods & 11\\
\bottomrule
\end{tabular}}
## Assessment
- Nine short weekly assignments, worth 2% or 4% each.
- One project due at the end of the semester, worth 20%.
- Exam (2 hours): 60%.
\pause
\begin{tabular}{lll}
\toprule
\textbf{Task} & \textbf{Due Date} & \textbf{Value} \\
\midrule
Assignments & Mon 11:59pm each week & 2 or 4\% each\\
Project & Fri 25 May & 20\% \\
Final exam & Official exam period & 60\% \\
\bottomrule
\end{tabular}
\pause
- Need at least 45\% for exam, and 50\% for total.
## Moodle site
- Includes all lecture notes, handouts, assignments
- Forum for asking questions, etc.
- Assignment submissions
## DataCamp
\centerline{\includegraphics[width=12.8cm]{datacampR}}
* All students must complete this course by Monday 5 March.
## DataCamp
\centerline{\includegraphics[width=13cm]{datacampforecasting}}
* We will do one chapter at a time throughout the semester.