Download Forecasting - Rob J Hyndman

Rob J Hyndman
Forecasting:
Principles and Practice
Forecasting: Principles and Practice
1
CASE STUDY 1: Paperware company
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 tell them what is
wrong and fix it.
Forecasting: Principles and Practice
2
CASE STUDY 1: Paperware company
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 tell them what is
wrong and fix it.
Additional information
Program written in COBOL making numerical calculations
limited. It is not possible to do any optimisation.
Forecasting: Principles and Practice
2
CASE STUDY 1: Paperware company
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 tell them what is
wrong and fix it.
Additional information
Program written in COBOL making numerical calculations
limited. It is not possible to do any optimisation.
Their programmer has little experience in numerical
computing.
Forecasting: Principles and Practice
2
CASE STUDY 1: Paperware company
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 tell them what is
wrong and fix it.
Additional information
Program written in COBOL making numerical calculations
limited. It is not possible to do any optimisation.
Their programmer has little experience in numerical
computing.
They employ no statisticians and want the program to
produce forecasts automatically.
Forecasting: Principles and Practice
2
CASE STUDY 1: Paperware company
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.
Forecasting: Principles and Practice
3
CASE STUDY 2: PBS
Forecasting: Principles and Practice
4
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.
Forecasting: Principles and Practice
5
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.
Forecasting: Principles and Practice
5
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.
Forecasting: Principles and Practice
5
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.
Forecasting: Principles and Practice
5
CASE STUDY 2: PBS
Forecasting: Principles and Practice
6
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 FORECAST
function in MS-Excel!
Problem: How to do the forecasting better?
Forecasting: Principles and Practice
7
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 FORECAST
function in MS-Excel!
Problem: How to do the forecasting better?
Forecasting: Principles and Practice
7
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 FORECAST
function in MS-Excel!
Problem: How to do the forecasting better?
Forecasting: Principles and Practice
7
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 FORECAST
function in MS-Excel!
Problem: How to do the forecasting better?
Forecasting: Principles and Practice
7
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 FORECAST
function in MS-Excel!
Problem: How to do the forecasting better?
Forecasting: Principles and Practice
7
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 FORECAST
function in MS-Excel!
Problem: How to do the forecasting better?
Forecasting: Principles and Practice
7
CASE STUDY 3: Airline
Forecasting: Principles and Practice
8
CASE STUDY 3: Airline
0.0
1.0
2.0
First class passengers: Melbourne−Sydney
1988
1989
1990
1991
1992
1993
1992
1993
1992
1993
Year
0 2 4 6 8
Business class passengers: Melbourne−Sydney
1988
1989
1990
1991
Year
0
10
20
30
Economy class passengers: Melbourne−Sydney
1988
1989
Forecasting: Principles and Practice
1990
1991
Year
9
CASE STUDY 3: Airline
0.0
1.0
2.0
First class passengers: Melbourne−Sydney
1991
Not1989
the real 1990
data!
Year
Or isBusiness
it? class passengers: Melbourne−Sydney
1992
1993
1992
1993
1992
1993
0 2 4 6 8
1988
1988
1989
1990
1991
Year
0
10
20
30
Economy class passengers: Melbourne−Sydney
1988
1989
Forecasting: Principles and Practice
1990
1991
Year
9
CASE STUDY 3: 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.
Forecasting: Principles and Practice
10
CASE STUDY 3: 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.
Forecasting: Principles and Practice
10
CASE STUDY 3: 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.
Forecasting: Principles and Practice
10
CASE STUDY 3: 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.
Forecasting: Principles and Practice
10
Resources
Slides
Exercises
Textbook
Useful links
robjhyndman.com/uwa
Forecasting: Principles and Practice
11
Useful resources
Organization:
ã International Institute of Forecasters.
Blog:
ã robjhyndman.com/hyndsight
Conferences:
ã International Symposium on Forecasting.
June 2015, Riverside, California.
Journals:
ã International Journal of Forecasting
ã Foresight
Links to all of the above at www.forecasters.org.
Forecasting: Principles and Practice
12
Useful resources
Organization:
ã International Institute of Forecasters.
Blog:
ã robjhyndman.com/hyndsight
Conferences:
ã International Symposium on Forecasting.
June 2015, Riverside, California.
Journals:
ã International Journal of Forecasting
ã Foresight
Links to all of the above at www.forecasters.org.
Forecasting: Principles and Practice
12
Applied Forecasting Journal
Issue 29
Spring 2013
THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING
THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING
5 Forecasting Revenue
in Professional Service Companies
14 Forecast Value Added:
A Reality Check on Forecasting Practices
19 S&OP and Financial Planning
26 CPFR: Collaboration Beyond S&OP
39 Progress in Forecasting Rare Events
50 Review of "Global Trends 2030:
Alternative Worlds"
Forecasting: Principles and Practice
13
Happy forecasting
A good forecaster is not smarter
than everyone else, he merely has
his ignorance better organised.
Forecasting: Principles and Practice
14