fpp 1. getting started
DESCRIPTION
Slides for ETC2450.Forecasting principles and practice: 1. Getting started.TRANSCRIPT
Applied forecasting forbusiness and economics
1. Getting startedOTexts.com/fpp/1/
ETC2450
Outline
1 A brief history of forecasting
2 Types of data
3 Forecasting models
4 Some case studies
5 The statistical forecasting perspective
6 Introduction to R
1. Getting started A brief history of forecasting 2
Standard business practice today
“What-if scenarios” based on assumed andfixed future conditions.
Highly subjective.
Not replicable or testable.
No possible way of quantifying probabilisticuncertainty.
Lack of uncertainty statements leads to falsesense of accuracy.
Largely guesswork.
Is this any better than a sheep’s liver orhallucinogens?
1. Getting started A brief history of forecasting 3
The rise of stochastic models
1. Getting started A brief history of forecasting 4
1959 exponential smoothing (Brown)1970 ARIMA models (Box, Jenkins)1980 VAR models (Sims, Granger)1980 non-linear models (Granger, Tong, Hamilton,
Teräsvirta, . . . )1982 ARCH/GARCH (Engle, Bollerslev)1986 neural networks (Rumelhart)1989 state space models (Harvey, West, Harrison)1994 nonparametric forecasting (Tjøstheim,
Härdle, Tsay,. . . )2002 exponential smoothing state space models
(Snyder, Hyndman, Koehler, Ord)
Advantages of stochastic models
Based on empirical data
Computable
Replicable
Testable
Objective measure of uncertainty
Able to compute prediction intervals
1. Getting started A brief history of forecasting 5
Outline
1 A brief history of forecasting
2 Types of data
3 Forecasting models
4 Some case studies
5 The statistical forecasting perspective
6 Introduction to R
1. Getting started Types of data 6
Types of data
Most forecasting problems use either
1 Time series data (collected at regular intervalsover time)
2 Cross-sectional data are for a single point intime.
Time series examples
Daily IBM stock pricesMonthly rainfallAnnual Google profitsQuarterly Australian beer production
Forecasting is estimating how the sequenceof observations will continue into the future.
1. Getting started Types of data 7
Australian beer production
1. Getting started Types of data 8
Year
meg
alite
rs
1995 2000 2005 2010
400
450
500
Types of data
Cross-sectional examples
House prices for all houses sold in 2009 inClayton. We are interested in predicting theprice of a house not in our data set using housecharacteristics: position, no. bedrooms, age,etc.
Fuel economy data for a range of 2009 modelcars. We are interested in predicting the carbonfootprint of a vehicle not in our data set usinginformation such as the size of the engine andthe fuel efficiency of the car.
1. Getting started Types of data 9
Vehicle carbon footprints
Model Cyl. Litres City Highway CarbonMPG MPG footprint
Chevrolet Aveo 4 1.6 25.0 34 6.6Chrysler PT Cruiser 4 2.4 19.0 24 8.7Dodge Avenger 4 2.4 21.0 30 7.7Ford Escape FWD 4 2.5 20.0 28 8.0Ford Ranger Pickup 2WD 4 2.3 19.0 24 8.7GMC Canyon 2WD 4 2.9 18.0 24 9.2Honda Accord 4 2.4 21.0 30 7.7Honda Civic 4 1.8 25.0 36 6.3. . .
All vehicles with automatic transmission and usingregular fuel. How to predict carbon footprint (tonsof CO2 per year) for other vehicles?
1. Getting started Types of data 10
Outline
1 A brief history of forecasting
2 Types of data
3 Forecasting models
4 Some case studies
5 The statistical forecasting perspective
6 Introduction to R
1. Getting started Forecasting models 11
Time series models
Time series models use only information on thevariable to be forecast
EDt+1 = f(EDt,EDt−1,EDt−2,EDt−3, . . . , error),
where t is time and ED is electricity demand.
e.g., ARIMA models and exponential smoothing.
Useful when predictor variables not known or measured.
Useful if prediction of predictor variables difficult.
Doesn’t lead to much understanding of system
1. Getting started Forecasting models 12
Cross-sectional models
Cross-sectional models assume that variable tobe forecast is affected by one or more otherpredictor variables.
ED = f(current temperature, GDP,
population, time of day, day of week,
error).
e.g., regression models.
1. Getting started Forecasting models 13
Mixed models
Mixed model
EDt+1 = f(EDt, current temperature,
time of day, day of week, error).
e.g., dynamic regression models, panel datamodels, longitudinal models, transfer functionmodels
1. Getting started Forecasting models 14
Outline
1 A brief history of forecasting
2 Types of data
3 Forecasting models
4 Some case studies
5 The statistical forecasting perspective
6 Introduction to R
1. Getting started Some case studies 15
CASE STUDY 1: Paperware company
Client: large company manufacturing disposable tableware.Problem: They want forecasts of each of hundreds of items.Series can be stationary, trended or seasonal. They currentlyhave a large forecasting program written in-house but itdoesn’t seem to produce sensible forecasts. They want me totell them what is wrong and fix it.Additional information
The program is written in COBOL making numericalcalculations limited. It is not possible to do anyoptimisation.
Their programmer has little experience in numericalcomputing.
They employ no statisticians and want the program toproduce forecasts automatically.
1. Getting started Some case studies 16
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’svalues.(Equivalent to differencing at lag 12 and takingmean.)
I Same as H except over 6 months.
K I couldn’t understand the explanation.
1. Getting started Some case studies 17
CASE STUDY 2: PBS
Client: Federal governmentProblem: Develop methodology to forecast annualbudget for Pharmaceutical Benefit Scheme (around$7billion).
Additional informationAt the time, they used Excel to fit a trend linethrough three observations from about 10years earlier.All calculations must be done in Excel.They have under-estimated expenditure bynearly $1billion in last two years.
1. Getting started Some case studies 18
CASE STUDY 3: Car fleet company
Client: One of Australia’s largest car fleetcompaniesProblem: how to forecast resale value of vehicles?How should this affect leasing and sales policies?
Additional information
They can provide a large amount of data onprevious vehicles and their eventual resalevalues.
The resale values are currently estimated by agroup of specialists. They see me as a threatand do not cooperate.
1. Getting started Some case studies 19
CASE STUDY 4: Airline
Client: Ansett.Problem: how to forecast passenger traffic onmajor routes.
Additional information
They can provide a large amount of data onprevious routes.
Traffic is affected by school holidays, specialevents such as the Grand Prix, advertisingcampaigns, competition behaviour, etc.
They have a highly capable team of people whoare able to do most of the computing.
1. Getting started Some case studies 20
Outline
1 A brief history of forecasting
2 Types of data
3 Forecasting models
4 Some case studies
5 The statistical forecasting perspective
6 Introduction to R
1. Getting started The statistical forecasting perspective 21
Statistical forecasting
Thing to be forecast: a random variable, yi.
Forecast distribution: If I is all observations,then yi|I means “the random variable yi givenwhat we know in I”.
The “point forecast” is the mean (or median) ofyi|IThe “forecast variance” is var[yi|I]A prediction interval or “interval forecast” is arange of values of yi with high probability.
With time series, yt|t−1 = yt|{y1, y2, . . . , yt−1}.yT+h|T = E[yT+h|y1, . . . , yT] (an h-step forecasttaking account of all observations up to time T).
1. Getting started The statistical forecasting perspective 22
Outline
1 A brief history of forecasting
2 Types of data
3 Forecasting models
4 Some case studies
5 The statistical forecasting perspective
6 Introduction to R
1. Getting started Introduction to R 23
Australian GDPausgdp <- ts(scan("gdp.dat"),frequency=4,
start=1971+2/4)Class: tsPrint and plotting methods available.
> ausgdpQtr1 Qtr2 Qtr3 Qtr4
1971 4612 46511972 4645 4615 4645 47221973 4780 4830 4887 49331974 4921 4875 4867 49051975 4938 4934 4942 49791976 5028 5079 5112 51271977 5130 5101 5072 50691978 5100 5166 5244 53121979 5349 5370 5388 53961980 5388 5403 5442 5482
1. Getting started Introduction to R 24
Australian GDP
1. Getting started Introduction to R 25
Time
ausg
dp
1975 1980 1985 1990 19954500
5000
5500
6000
6500
7000
7500 > plot(ausgdp)
Residential electricity sales
> elecsalesTime Series:Start = 1989End = 2008Frequency = 1[1] 2354.34 2379.71 2318.52 2468.99 2386.09 2569.47[7] 2575.72 2762.72 2844.50 3000.70 3108.10 3357.50
[13] 3075.70 3180.60 3221.60 3176.20 3430.60 3527.48[19] 3637.89 3655.00
1. Getting started Introduction to R 26
Credit scores
credit <- read.table("bankdata.csv",header=TRUE, sep=",")
Class: data.frame
Print and plotting methods available.> head(credit)
score savings income fte single time.address time.employed3282 39.39981 0.012 111.168 TRUE FALSE 27 85018 51.79090 0.654 56.400 TRUE FALSE 29 338317 32.81704 0.748 36.744 TRUE TRUE 2 1613766 57.30881 0.616 55.992 TRUE TRUE 14 72325 37.17328 4.132 62.040 TRUE TRUE 2 1413573 33.68829 0.000 43.752 TRUE TRUE 7 7
1. Getting started Introduction to R 27
Credit scores
1. Getting started Introduction to R 28
score
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0 40 80
040
80
time.employed
plot(credit)
Class package
> library(fpp)This loads:
some data for use in examples and exercisesforecast package (for forecasting functions)tseries package (for a few time seriesfunctions)fma package (for lots of time series data)expsmooth package (for more time seriesdata)lmtest package (for some regressionfunctions)
1. Getting started Introduction to R 29