1-getting-started.qmd

---
title: "ETC3550/ETC5550 Applied forecasting"
author: "Ch1. Getting started"
institute: "OTexts.org/fpp3/"
pdf-engine: pdflatex
fig-width: 7.5
fig-height: 3.5
format:
  beamer:
    theme: monash
    aspectratio: 169
    fontsize: 14pt
    section-titles: false
    knitr:
      opts_chunk:
        dev: "cairo_pdf"
include-in-header: header.tex
execute:
  echo: false
  message: false
  warning: false
---

```{r setup, include=FALSE}
source("setup.R")
library(patchwork)
library(gganimate)
austa <- readr::read_csv("http://OTexts.com/fpp3/extrafiles/austa.csv") |>
  as_tsibble(index = Year)
melsyd <- tsibbledata::ansett |>
  filter(Airports == "MEL-SYD")
```

## CASE STUDY 1: Paperware company

\fontsize{11.5}{13}\sf

\begin{textblock}{9.2}(0.2,1.5)
\textbf{Problem:} Want forecasts of each of hundreds of
items. Series can be stationary, trended or seasonal. They currently
have a large forecasting program written in-house but it doesn't seem
to produce sensible forecasts. They want me to fix it.

\textbf{Additional information}\vspace*{-0.2cm}\fontsize{11.5}{13}\sf
\begin{itemize}\itemsep=0cm\parskip=0cm
\item  Program  written in COBOL making numerical calculations limited. It is not possible to do any optimisation.
\item Their programmer has little experience in numerical computing.
\item They employ no statisticians and want the program to produce forecasts automatically.
\end{itemize}
\end{textblock}

\placefig{10.2}{1.4}{width=5.8cm}{tableware2}

## CASE STUDY 1: Paperware company
\vspace*{0.2cm}

### Methods currently used

A
: 12 month average

C
: 6 month average

E
: straight line regression over last 12 months

G
: straight line regression over last 6 months

H
: average slope between last year's and this year's values.
  (Equivalent to differencing at lag 12 and taking mean.)

I
: Same as H except over 6 months.

K
: I couldn't understand the explanation.

## CASE STUDY 2: PBS

\fullwidth{pills}

## CASE STUDY 2: PBS

### The Pharmaceutical Benefits Scheme (PBS) is the Australian government drugs subsidy scheme.

  * Many drugs bought from pharmacies are subsidised to allow more equitable access to modern drugs.
  * The cost to government is determined by the number and types of drugs purchased. Currently nearly 1\% of GDP.
  * The total cost is budgeted based on forecasts of drug usage.

## CASE STUDY 2: PBS

\fullheight{pbs2}

## CASE STUDY 2: PBS

  * In 2001: \$4.5 billion budget, under-forecasted by \$800 million.
  * Thousands of products. Seasonal demand.
  * Subject to covert marketing, volatile products, uncontrollable expenditure.
  * Although monthly data available for 10 years, data are aggregated to annual values, and only the first three years are used in estimating the forecasts.
  * All forecasts being done with the \texttt{FORECAST} function in MS-Excel!

## CASE STUDY 3: Car fleet company

**Client:** One of Australia's largest car fleet companies

**Problem:** how to forecast resale value of vehicles? How
should this affect leasing and sales policies?

\pause

### Additional information
 - They can provide a large amount of data on previous vehicles and their eventual resale values.
 - The resale values are currently estimated by a group of specialists. They see me as a threat and do not cooperate.

## CASE STUDY 4: Airline

\fullheight{ansettlogo}

## CASE STUDY 4: Airline

```{r, echo=FALSE}
melsyd |>
  filter(Class == "Economy") |>
  autoplot(Passengers) +
  labs(
    title = "Economy class passengers",
    subtitle = "Melbourne-Sydney",
    x = "Year",
    y = "Thousands"
  )
```

\only<2>{\begin{textblock}{4.2}(11,5.5)
\begin{alertblock}{}
Not the real data! Or is it?
\end{alertblock}
\end{textblock}}

## CASE STUDY 4: Airline

**Problem:** how to forecast passenger traffic on major routes?

### Additional information

  * They can provide a large amount of data on previous routes.
  * Traffic is affected by school holidays, special events such as
the Grand Prix, advertising campaigns, competition behaviour, etc.
  * They have a highly capable team of people who are able to do
most of the computing.