Overview of MethodsOverview of Methods

Quantitative TechniquesQuantitative Techniques

Moving AverageMoving AverageTrend AnalysisTrend AnalysisExponential SmoothingExponential SmoothingARIMA modelsARIMA modelsEconometric modelsEconometric models

Moving AverageMoving Average

A simple average of the previous X A simple average of the previous X months/yearsmonths/years

A six-month moving average forecast is an A six-month moving average forecast is an average of the previous six monthsaverage of the previous six months

““I always avoid prophesying I always avoid prophesying beforehand because it much beforehand because it much better to prophesy after the better to prophesy after the

event has already taken place.”event has already taken place.”

Winston ChurchillWinston Churchill

Moving Average – When To UseMoving Average – When To Use

Extremely “noisy” or little dataExtremely “noisy” or little data

Time constraintTime constraint

Degree of accuracy not importantDegree of accuracy not important

Moving Average - AdvantagesMoving Average - Advantages

Extremely simpleExtremely simple

Easy to implementEasy to implement

Moving Average - DisadvantagesMoving Average - Disadvantages

Not accurate; slow adjustment to changes Not accurate; slow adjustment to changes in datain data

Misses turning pointsMisses turning points

All history is created equalAll history is created equal

Moving Average ExampleMoving Average Example

Trend RegressionTrend Regression

A straight ( or curved) line drawn through A straight ( or curved) line drawn through historical datahistorical data

““taking a ruler through your data”taking a ruler through your data”

““The best qualification of a The best qualification of a prophet is to have a good prophet is to have a good

memory.”memory.”

Marquis of HalifaxMarquis of Halifax

Trend Regression – When To UseTrend Regression – When To Use

Steady rise or decline in dataSteady rise or decline in data

Time or software constraintTime or software constraint

Need easy explanationNeed easy explanation

Little dataLittle data

Trend Regression - AdvantagesTrend Regression - Advantages

Very simpleVery simple

Can be done in ExcelCan be done in Excel

Trend Regression – DisadvantagesTrend Regression – Disadvantages

Assumes future is exactly like past (prices, Assumes future is exactly like past (prices, economy, etc.)economy, etc.)

All history is created equalAll history is created equal

One bad data point can greatly affect One bad data point can greatly affect forecastforecast

Trend Regression ExampleTrend Regression Example

Exponential SmoothingExponential Smoothing

SimpleSimple

Double (Brown) or HoltDouble (Brown) or Holt

WintersWinters

““A good forecaster is not A good forecaster is not smarter than everyone else, he smarter than everyone else, he merely has his ignorance better merely has his ignorance better

organized.”organized.”

C. W. J. GrangerC. W. J. Granger

Simple Exponential SmoothingSimple Exponential Smoothing

Weighted average of past values with Weighted average of past values with exponentially decreasing weightsexponentially decreasing weights

Forecast this month equals last month’s Forecast this month equals last month’s forecast plus a proportion of the forecast forecast plus a proportion of the forecast error last montherror last month

Simple Exponential Smoothing – Simple Exponential Smoothing – When To UseWhen To Use

Stationary data with no trend or Stationary data with no trend or seasonalityseasonality

Double (Brown) or Holt Exponential Double (Brown) or Holt Exponential SmoothingSmoothing

Smooth the smoothed data with a Smooth the smoothed data with a weighted average of past values with weighted average of past values with exponentially decreasing weightsexponentially decreasing weights

Changes linearly with time (like linear Changes linearly with time (like linear regression) with recent data given more regression) with recent data given more weightweight

Double (Brown) or Holt Exponential Double (Brown) or Holt Exponential Smoothing – When to UseSmoothing – When to Use

Data with a trend but no seasonalityData with a trend but no seasonality

Winter’s Exponential SmoothingWinter’s Exponential Smoothing

Deseasonalize data, then find trend, then Deseasonalize data, then find trend, then smoothsmooth

Winter’s Exponential Smoothing – Winter’s Exponential Smoothing – When to UseWhen to Use

Data with trend and seasonalityData with trend and seasonality

Exponential Smoothing AdvantagesExponential Smoothing Advantages

Somewhat simpleSomewhat simple

Recent data given more weightRecent data given more weight

Fairly good accuracy for short-term Fairly good accuracy for short-term forecastsforecasts

Software can automate processSoftware can automate process

Exponential Smoothing - Exponential Smoothing - DisadvantagesDisadvantages

Requires forecasting softwareRequires forecasting software

Bad data in recent month can cause great Bad data in recent month can cause great error in forecasterror in forecast

Less accurate for medium to long-term Less accurate for medium to long-term forecastsforecasts

Assumes history is like (recent) historyAssumes history is like (recent) history

Exponential Smoothing ExampleExponential Smoothing Example

ARIMA (Box-Jenkins) ModelsARIMA (Box-Jenkins) Models

AAutoutoRRegressive egressive IIntegrative ntegrative MMoving oving AAverageverage

AutoregressiveAutoregressive – future values depend on – future values depend on previous values of the dataprevious values of the data

Moving averageMoving average – future values depend on – future values depend on previous values of the errorsprevious values of the errors

IntegratedIntegrated – refers to differencing the data – refers to differencing the data

““An unsophisticated forecaster An unsophisticated forecaster uses statistics as a drunken uses statistics as a drunken man uses lamp-posts – for man uses lamp-posts – for

support rather than illumination”support rather than illumination”

- after Andrew Lang- after Andrew Lang

ARIMA (Box-Jenkins) Models – ARIMA (Box-Jenkins) Models – When to UseWhen to Use

Stable data that has regular correlationsStable data that has regular correlations

ARIMA ( Box-Jenkins) Models - ARIMA ( Box-Jenkins) Models - AdvantagesAdvantages

Outperforms exponential smoothing on Outperforms exponential smoothing on homogenous and stable datahomogenous and stable data

Software can automateSoftware can automate

Sounds impressiveSounds impressive

ARIMA (Box-Jenkins) Models - ARIMA (Box-Jenkins) Models - DisadvantagesDisadvantages

Requires softwareRequires software

Needs a minimum of 40 data pointsNeeds a minimum of 40 data points

Complicated to understandComplicated to understand

ARIMA (Box-Jenkins) Models ARIMA (Box-Jenkins) Models ExampleExample

Econometric ModelsEconometric Models

Relates data series to explanatory Relates data series to explanatory variablesvariables

Economists build demand models which Economists build demand models which relaterelate– Price, competition, income, population, etc.Price, competition, income, population, etc.

““An economist is an expert who An economist is an expert who will know tomorrow why the will know tomorrow why the

things he predicted yesterday things he predicted yesterday didn’t happen today.”didn’t happen today.”

Evan EsarEvan Esar

Econometric Models – When to Econometric Models – When to UseUse

Important to understand marketImportant to understand market

Influences on product demand are Influences on product demand are changingchanging

Historically more acceptable in regulationHistorically more acceptable in regulation

Econometric Models - AdvantagesEconometric Models - Advantages

Can give price elasticityCan give price elasticity

Formally integrates economic impactFormally integrates economic impact

Permits varied assumptions, i.e., “what if?”Permits varied assumptions, i.e., “what if?”

Forces you to make assumptions explicitForces you to make assumptions explicit

Methods to deal with short timeMethods to deal with short time

Econometric Models - Econometric Models - DisadvantagesDisadvantages

Large data gatheringLarge data gathering

Expertise to buildExpertise to build

Requires forecasts of explanatory Requires forecasts of explanatory variablesvariables

Not always best forecasting techniqueNot always best forecasting technique

Econometric Models ExampleEconometric Models Example

Res. DA ModelRes. DA Model

#DA calls per person = 4.49-0.18*Price #DA calls per person = 4.49-0.18*Price +1.04*Income per +1.04*Income per person+0.00016*Timetrendperson+0.00016*Timetrend

SummarySummary

Graph dataGraph data

Choose appropriate technique forChoose appropriate technique for– OutputOutput– TimeTime– DataData

Know advantages and disadvantagesKnow advantages and disadvantages