1 chapter 7: forecasting 7.1 introduction 7.2 time series characteristics and components 7.3...
TRANSCRIPT
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Chapter 7: Forecasting
7.1 Introduction
7.2 Time Series Characteristics and Components
7.3 Introduction to SAS Forecast Studio
7.4 Time Series Regression Models
7.5 Time Series Data and Hierarchical Data Structure
7.6 Recommended Reading
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Chapter 7: Forecasting
7.1 Introduction7.1 Introduction
7.2 Time Series Characteristics and Components
7.3 Introduction to SAS Forecast Studio
7.4 Time Series Regression Models
7.5 Time Series Data and Hierarchical Data Structure
7.6 Recommended Reading
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Objectives Name some business applications of time series
modeling. Define basic time series concepts and approaches.
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Prediction in Time: ForecastingYou learned about statistical models to predict some outcome variable (buy/no buy, revenue, payoff/default).
Sometimes it is more important to predict what a particular variable will be in the future and at different points in time.
In these situations, the data could be based on an accumulation of measurements from customers (revenue), a physical process (customer service wait times), or even a technological phenomenon (demand on a server).
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Prediction in Time: ForecastingThe data for forecasting is known as a time series. Hence, the name time series forecasting refers to the general modeling approach.
Time series forecasting involves the prediction of future values of a response or the interpretation of what produced changes that were observed in a series over time.
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
How many big-screen TVs are you likely to sell this week?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
Should you use this shelf space for more peanut butter or for more salsa?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
What time of day produces peak server demand? Can you allocate more resources at that time?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
How many tables can you expect to fill at your restaurant on Valentine’s Day?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
If you put the item on sale this week, will demand go down next week?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
How much in ticket sales can you expect on Thursday?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
Is there a cyclical pattern to the number of purchases made on your Web site over a week?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
PricingWhen should you reorder raw materials?
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Examples of Forecasting in BusinessUnits in inventory
Demand on a system
Customer activity
Revenue
Transactions
Manufacturing supplies
Pricing
What are the pricing trends in the past quarter, compared to the previous three years?
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Idea ExchangeName a specific example variable that you might want to forecast. Would you want to have daily forecasts? Weekly? Yearly?
What type of data would you be able to access to obtain forecasts?
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Example: Singapore Unemployment
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Example: Singapore UnemploymentThe time series includes information that is gathered over time is for equally spaced time intervals uses the same measurement at each time enables the visualization of a pattern over time enables the quantification of such patterns enables forecasts to be made for future time points
based on past behavior.
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Example: Ice CreamYou are CEO of a large ice cream producer.
Profitability depends on accomplishing goals in three key areas: Supply chain activities for the coming year must be
coordinated and started. Production schedules must be set for the next three months. Strategic pricing initiatives and
promotional campaigns need to be assessed and approved.
Consider the key components of a time series.
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Ice Cream Demand
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Ice Cream Demand: Seasonal Cycle
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Ice Cream Demand: Trend
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Ice Cream Demand: Effects of Independent Variables
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Ice Cream DemandBy evaluating and quantifying the effects described for the ice cream demand example, you can do the following: forecast with some confidence how much ice cream you are likely
to sell each month (and hence how much you should produce) make choices about when you should make a special promotion
(to uplift an anticipated sales slump) estimate how much difference
those promotions are likely to make
identify when something unexpected occurred (such as an undiscovered competitor taking market share)
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Two Basic Approaches in Time Series Analysis Inference-based: what happened
– Policy or intervention evaluation– Marketing mix evaluation– Scenario evaluation or sensitivity analysis
Forecasting-based: what is likely to happen– Logistical decisions– Tactical decisions– Strategic decisions
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Two Basic Approaches in Time Series Analysis Inference-based: what happened
– Policy or intervention evaluation– Marketing mix evaluation– Scenario evaluation or sensitivity analysis
Forecasting-based: what is likely to happen– Logistical decisions– Tactical decisions– Strategic decisions
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7.01 PollTime series forecasting is concerned only with obtaining good forecasts of future values and not with understanding why a series changed.
Yes
No
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7.01 Poll – Correct AnswerTime series forecasting is concerned only with obtaining good forecasts of future values and not with understanding why a series changed.
Yes
No
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Idea ExchangeConsider the example that you gave of a forecasting variable earlier. Are you more interested in inference-based analysis, or forecast-based analysis? Or both? Why? Give specific examples.
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Chapter 7: Forecasting
7.1 Introduction
7.2 Time Series Characteristics and Components7.2 Time Series Characteristics and Components
7.3 Introduction to SAS Forecast Studio
7.4 Time Series Regression Models
7.5 Time Series Data and Hierarchical Data Structure
7.6 Recommended Reading
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Objectives Explain basic time series concepts and approaches. List the elements of a time series. Provide examples of time series models.
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The Universal Time Series Model
),,,( ttttt EXSTfY
TREND
SEASONAL ERROR (Irregular)
INPUT
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Airline Passengers 1990-2004
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Airline Passengers 1994-1997
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Statistical Time Series
Interval=1 month
A statistical time series isan indexed set of numbers. The index can consist of dates or other numbers.Many business time series are equally spaced.
A time series is equally spaced if any two consecutive indices havethe same interval time difference.
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Equally Spaced Time Series
Equally spaced time series
Unequally spaced time series
Equally spaced time serieswith missing values
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7.02 Multiple Choice PollTime series data must be
a. equally spaced data with no missing times
b. equally spaced, with missing values padded in the response column, if necessary
c. equally or unequally spaced, as long as missing intervals are indicated by either missing values or a skip in the time index column.
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7.02 Multiple Choice Poll – Correct AnswerTime series data must be
a. equally spaced data with no missing times.
b. equally spaced, with missing values padded in the response column, if necessary.
c. equally or unequally spaced, as long as missing intervals are indicated by either missing values or a skip in the time index column.
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The Universal Time Series Model
),,,( ttttt EXSTfY
TREND
SEASONAL ERROR (Irregular)
INPUT
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Airline Passengers 1994–1997: Trend
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Time Series TrendTrend usually refers to a deterministic function of time. Time series can be made of deterministic and
stochastic components. A stochastic component is subject to random variation
and can never be predicted perfectly except by chance.
A deterministic component exhibits no random variation and can be predicted perfectly. Common deterministic trend functions include linear trend, curvilinear trend, logarithmic trend, and exponential trend.
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Deterministic Trend ModelsLinear Trend
Quadratic Trend
tYt 10
2210 ttYt Y
t
Y
t
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Notation
tYt 10
Time series
Time index Parameters Time index
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Stochastic Trend ModelsRandom Walk
Random Walk with Drift
continued...
ttt EYY 1
ttt EYY 1
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Accommodating Stochastic Trend: Differencing
A First Difference of the Random Walk Process
1
1
ttt
ttt
YYY
EYY
First Difference
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7.03 PollDeterministic trends are accommodated in time series models through differencing.
Yes
No
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7.03 Poll – Correct AnswerDeterministic trends are accommodated in time series models through differencing.
Yes
No
No. Stochastic trends are accommodated through differencing.
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The Universal Time Series Model
),,,( ttttt EXSTfY
TREND
SEASONAL ERROR (Irregular)
INPUT
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Airline Passengers 1994–1997: Seasonal
August
February
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SeasonalityThe seasonal component of a time series represents the effects of seasonal variation. The foundation of seasonal variation is one or more
of the cycles produced by the motion of the celestial bodies in the solar system, dominated by the earth circling the sun every year. Another influential activity is the moon circling the earth approximately every 28 days.
The most general meaning of seasonality is a component that describes repetitive behavior at known seasonal periods. If the seasonal period is integer S, then seasonal factors are factors that repeat every S units of time.
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Accommodating Seasonal Components Trigonometric functions (sine waves) Seasonal dummy variables Seasonal differences (Box-Jenkins modeling) Seasonal model components (ESM models)
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Dummy Variables A dummy variable is an indicator variable. To indicate a specific time point, a dummy variable
takes one as the value for that time point. At all other time points, it takes zero as the value.
otherwise 0
2001 Sep when 1 tI t
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Seasonal Dummy VariablesFor a time series with S seasons, there are S dummy variables, one for each season.
Monthly Data: IJAN , IFEB ,…, IDEC
Daily Data: ISUN , IMON ,…, ISAT
Quarterly Data: IQ1 , IQ2 , IQ3 , IQ4
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Stochastic Seasonal Functions: Seasonal DifferencingFor seasonal data with period S, express the current value as a function that includes the value S time units in the past.
Yt = Yt-S + TRENDt + IRREGULARt
SYt = Yt Yt-S is called a difference of order S.
Examples:
Monthly: This January is a function of last January and so on.
Daily: This Sunday is a function of last Sunday and so on.
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7.04 PollSeasonal data can be accommodated through differencing.
Yes
No
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7.04 Poll – Correct AnswerSeasonal data can be accommodated through differencing.
Yes
No
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The Universal Time Series Model
),,,( ttttt EXSTfY
TREND
SEASONAL ERROR (Irregular)
INPUT
57
The Irregular Component The irregular component of a time series is what
remains when trend, seasonal, and input effects are removed.
The irregular component need not represent a random sequence of uncorrelated values. However, most models specify that the irregular component must be stationary.
A stationary time series has a constant mean and variance at all time points.
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Additive Decomposition of the Airline Data
T: LinearTrend
S: SeasonalAverage
I: IrregularComponent
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Analysis of Residuals (Forecast Error)
Residuals for Additive Decomposition Model
-3000000
-2000000
-1000000
0
1000000
2000000
3000000
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Time Index
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The Universal Time Series Model
),,,( ttttt EXSTfY
TREND
SEASONAL ERROR (Irregular)
INPUT
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Airline Passengers 1990-2004
Events
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Event Examples Retail promotions Advertising campaigns Negative article in a major publication Nickel Beer Thursday Mergers and acquisitions Government legislated policy changes Organizational personnel and/or policy changes Christmas Strikes Scandal Injury, illness, or death of a key player
(such as a CEO, CFO, or chief scientist)
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How Do Event Variables Improve Accuracy? Event variables enable the forecast model to
accommodate discrete shifts, also called jumps or bangs, in time series data.
Event variables in time series models are primarily intercept shifters.
Intercept shifters are included in the model as explanatory variables and are based on columns of 0s and 1s in the data set.
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How Do Event Variables Improve Accuracy?The data is fit with a linear model: tt trendsales
Bias
Data jump at Date = T* causesa large residual.
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How Do Event Variables Improve Accuracy?The linear model can be refined by modifying the intercept term as follows:
When Date = T*, the model’s intercept = ( + ), and when Date ≠ T*, the model’s intercept = .
otherwse0andTDateif1
)(*
D
trendDsales tt
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Event Variable Creation
BigStormEventT* = '01AUG2003'd
Resulting DummyColumn = D
00…010…0
Demand HistoryFor Sales
01JAN200201FEB2002
…01JUL200301AUG200301SEP2003
…01JUN2003
The temporary intercept shift is accomplished by adding a 0-1 or dummy column to the data.
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How Do Event Variables Improve Accuracy?The data is fit with a linear model and a pulse event variable.
Less biased forecast
The residual is much smaller.
trendDsalest )(
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Event Variable Qualifiers The event variable discussed above is a pulse type.
The pulse event variable qualifies variation in the data as follows:– There is a discrete shift in the data at Date = T*.
Before and after Date = T*, the series is at its steady-state intercept and slope.
– That is, the series is impacted only for one time interval: Date = T*.
How might the linear model be refined if the shift in the data resembles what is depicted on the next slide?
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How Do Event Variables Improve Accuracy?The data is fit with a linear model.
trendsalest
A permanent Intercept shift at this date
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How Do Event Variables Improve Accuracy?The linear model can be refined by modifying the intercept term as follows:
otherwse0andTDateif1
)(*
D
trendDsales tt
This is the same model specification as before, but the dummy column is changed as shown on the nextslide.
When Date => T*, the model’s intercept = ( + ), and when Date < T*, the model’s intercept = .
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Event Variable Creation
New Law EnactedT* = '01AUG2003'd
Resulting DummyColumn = D
00…011…1
Demand HistoryFor Sales
01JAN200201FEB2002
…01JUL200301AUG200301SEP2003
…01JUN2003
The permanent intercept shift is accomplished by adding a 0-1 or dummy column to the data.
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How Do Event Variables Improve Accuracy?The data is fit with a linear model and a step event variable.
The permanent shift is accommodated in the model.
trendDsalest )(
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Event Variable Qualifiers The event variable discussed above is a step type. The step event qualifies variation in the data as
follows: – There is a discrete shift in the data at Date = T*.
Before Date = T*, the series is at its pre-event, steady-state intercept. When Date => T*, the series it at a new, steady state intercept.
– That is, the series is impacted permanently; on and after Date = T*, the series has a new intercept.
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Basic Event Variable Types
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Idea ExchangeCompare the use of event variables for events that can be foreseen (such as Christmas holiday) to events that cannot be foreseen (such as a major storm).
How would these two types of events change the usefulness of your forecasts?
How would they change how you would make business decisions based on the forecasts?
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Chapter 7: Forecasting
7.1 Introduction
7.2 Time Series Characteristics and Components
7.3 Introduction to SAS Forecast Studio7.3 Introduction to SAS Forecast Studio
7.4 Time Series Regression Models
7.5 Time Series Data and Hierarchical Data Structure
7.6 Recommended Reading
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Objectives Create a project and generate forecasts for a single
series. Explain the basics of navigating SAS Forecast Studio
and interpreting results.
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SAS Forecast Studio
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SAS Forecast Studio: Interface Tour
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SAS Forecast Studio: Interface Tour Menu Bar and Shortcut Buttons
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SAS Forecast Studio: Interface Tour
Menu Bar andShortcut Buttons
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SAS Forecast Studio: Interface Tour
The Active Series
OverviewPanel
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SAS Forecast Studio: Interface Tour
The Four View Tabs
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SAS Forecast Studio: Interface Tour
The Forecasting View
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SAS Forecast Studio: Interface Tour
The Modeling View
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SAS Forecast Studio: Interface Tour
The Model Selection List (MSL) is associatedwith the highlighted series.
The Modeling View
MSL
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SAS Forecast Studio: Interface Tour
The Series View
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SAS Forecast Studio: Interface Tour
The ScenarioAnalysis View
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The Forecasting Workflow
Forecasting workflow
Def
ine
fore
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obje
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Sele
ct s
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peci
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Valid
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s an
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Rep
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App
ly a
naly
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and
gene
rate
fore
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Run
ana
lysi
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Ass
ess
obse
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resu
lts
Ref
ine
fore
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obje
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Acc
umul
ate
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s; c
reat
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erar
chy
Dis
sem
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Acc
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odat
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pdat
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Rec
onci
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orec
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App
ly fo
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verr
ides
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Large-Scale Forecasting Scenario
Time Series Data
...
80% can be forecast automatically.
10% requires extra effort.
10% cannot be forecast accurately.
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Large-Scale Forecasting Scenario
Time Series Data
80% can be forecast automatically.
10% requires extra effort.
10% cannot be forecast accurately.
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SuperToys Inc.SuperToys Inc. ran a campaign to promote sales of a classic line of dolls. Your job is to measure the impact of the promotion
and determine whether the promotion should be run in the future, perhaps to promote Christmas sales.
Forecast the sales for the next several weeks and quantify the effect of the discount promotion on weekly sales.
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The DataREG2_GBTOYS is a time series data set with the following: weekly data for a popular doll in all toy
stores averaged over four sales regions the number of units sold per region
each week (units) information about when special
discount promotions were implemented (pctpromo)
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Generating Forecasts Automatically
Toy Case Study
Task: Use the REG2_GBTOYS data to forecast a single series with a discount promotion.
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Use of Accuracy CriteriaTwo ways to judge accuracy: Accuracy can be calculated for one-step-ahead
forecasts over the entire range of the data. Accuracy can be calculated for a holdout sample of
data at the end of each time series that was not used to construct models. A time series might be too short to enable use of a holdout sample. This method is preferred, but it is often not feasible. Using a holdout sample to judge accuracy is often referred to as honest assessment because it simulates fitting and deploying a model and then judging accuracy in the live environment.
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Assessing Generated Models
Toy Case Study
Tasks: Compare different candidate models to select the best one, and evaluate the parameter estimates produced by the model.
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Creating a Scenario Analysis
Toy Case Study
Task: Create a scenario analysis that evaluates the effect of a promotional campaign on future forecasts.
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Idea ExchangeYou could do more with scenario analysis. For example, if management wants to clear 250 extra dolls from inventory, how many weeks of sales promotion would be needed to sell 250 above and beyond the baseline forecast?
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Exercise
This exercise reinforces the concepts discussed previously.
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Chapter 7: Forecasting
7.1 Introduction
7.2 Time Series Characteristics and Components
7.3 Introduction to SAS Forecast Studio
7.4 Time Series Regression Models7.4 Time Series Regression Models
7.5 Time Series Data and Hierarchical Data Structure
7.6 Recommended Reading
101
Objectives Describe the basic assumptions of static regression
modeling. Explain the basics of dynamic regression models. Define the role of transfer functions in time series
regression models.
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Static RegressionIn static (non-time series) regression analysis, the predictors and the response are assumed to occur together in time.
DISCOUNT(today) INCREASED SALES(today)
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Static RegressionHowever, it might be more important to understand how predictors affect a response at a different time.
DISCOUNT(today) INCREASED SALES (today) and DECREASED SALES (tomorrow)
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Static Linear RegressionAssumptions The predictor variables are known and measured
without error. The functional relationship between inputs and target
is linear. The error term represents a set of random variables
that are independent and identically distributed with a normal distribution having a mean of 0 and a variance of σ2.
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From Static Regression to Time Series RegressionThe time series regression model is an extension of the static regression model in which variables are observed in time autocorrelation is allowed the target variable can be influenced by past values of
inputs.
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Time Series Regression TerminologyOrdinary Regressor An input variable that has only a concurrent influence
on the target variable: X at time t is correlated with Y at time t. Variation in X at times before and after t is uncorrelated with Y at time t.
Dynamic Regressor An input variable that influences the target variable at
current and future values: variation in X at time t can influence Y at time t, t + 1, t + 2, ….
Transfer Function A function that provides the mathematical relationship
between a dynamic regressor and the target variable.
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7.05 Multiple Choice PollDynamic regressors require special treatment because they
a. change value frequently.
b. relate to the response at time t, and at subsequent times as well.
c. cannot be known in advance.
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7.05 Multiple Choice Poll – Correct AnswerDynamic regressors require special treatment because they
a. change value frequently.
b. relate to the response at time t, and at subsequent times as well.
c. cannot be known in advance.
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Types of Regressors: Measurement ScaleBinary (Dummy) Variables Take the value 0 or 1 Can be used to quantify nominal data
Categorical Variables Nominal scaled nonquantitative categories Ordinal scaled variables can be treated as categorical Must be coded into a quantitative input, usually using
a form of dummy coding for each level (less one if a constant term is used in the model)
Quantitative Variables Interval or ratio scaled Can be transformed
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Types of Regressors: RandomnessDeterministic Controlled by experimenter Can be perfectly predicted without error
Stochastic Governed by unknown probability distributions Cannot be perfectly predicted
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Advertising Case StudyAn online retail firm advertises in several media channels. With an ever-changing electronic marketplace, you must determine how to best allocate your advertising budget.
Find the optimal mix of advertising spending across
Internet Television Radio
Print media Direct mail
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The Data Predictor Variable Description
DirectMail Weekly direct mail advertising (x$1000)
Internet Weekly Internet advertising (x$1000)
PrintMedia Weekly print media advertising (x$1000)
SalesRatio Ratio of competitor sales to total known sales
TVRadio Weekly TV/radio advertising (x$1000)
Response Variable Description
SalesAmount Total sales revenue for all customers (x$1000)
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Comparing Advertising Effectiveness
Advertising Case Study
Task: Compare the effectiveness of different advertising channels, estimating the increase per dollar spent on direct mailings, Internet ads, print media ads, and TV/radio airtime.
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Chapter 7: Forecasting
7.1 Introduction
7.2 Time Series Characteristics and Components
7.3 Introduction to SAS Forecast Studio
7.4 Time Series Regression Models
7.5 Time Series Data and Hierarchical Data 7.5 Time Series Data and Hierarchical Data StructureStructure
7.6 Recommended Reading
115
Objectives Explain the process of converting time-stamped data
into time series data: accumulation. Describe various accumulation options in the software. Explain the process of building the data hierarchy. Describe various aggregation options in the software.
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Transactional Data Time BinningMonthly Time Bins – Only the past two years are shown.
Some time bins have no recorded observations.
Some time bins have more than one observation.
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Producing Time Series DataData Accumulation Accumulates transactional data to the specified time
interval of the data
Data Aggregation Generates time series
data for upper levels (by groups) of the data hierarchy
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Accumulating Unequally Spaced Data Time-stamped transactional data is rarely spaced
equally. Transactional data can be accumulated to make it
spaced equally. There are several accumulation options, including the
following:– Total (sum over accumulated period)– Average– Median– Minimum or Maximum– First or Last– Others based on summary statistics (STD, CSS,
USS, N, NOBS, NMISS)
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Transactional Data AccumulationFor this time-stamped data, accumulating on a monthly average basis is different from accumulating on a total basis.
Choose an accumulation method that makes sense given your series.
ACCUMULATE=TOTAL ACCUMULATE=AVERAGE
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Producing Time Series DataData Accumulation Accumulates transactional data to the specified time
interval of the data
Data Aggregation Generates time series
data for upper levels (by groups) of the datahierarchy
121
7.06 Multiple Answer PollChoose the statements below that are correct.
a. Data aggregation entails combining different series to form a hierarchy.
b. Data accumulation entails combining different series to form a hierarchy.
c. Data aggregation entails rolling up more frequent time intervals to produce fewer intervals.
d. Data accumulation entails rolling up more frequent time intervals to produce fewer intervals.
122
7.06 Multiple Answer Poll – Correct AnswersChoose the statements below that are correct.
a. Data aggregation entails combining different series to form a hierarchy.
b. Data accumulation entails combining different series to form a hierarchy.
c. Data aggregation entails rolling up more frequent time intervals to produce fewer intervals.
d. Data accumulation entails rolling up more frequent time intervals to produce fewer intervals.
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Data Hierarchies: Aggregation
Data in the middle and upper levels of the hierarchy is constructed from data in the base level of the hierarchy. Above, group-level data is created by adding together department-level series. The top level (for example, total sales) is created by adding all base-level series.
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BY Variables in Hierarchical Data BY variables group observations that have the same
value for the BY variable.
Assigning a BY variable enables you to obtain separate analyses for groups of observations.
For hierarchical time series, the order of the BY variables describes the structure of the hierarchy.
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Aggregated DataThe group and value series are constructed from the accumulated dept series.
The chart is an abstract representation of the hierarchy.
126
Wine Case StudyWINECO has, until recently, focused on value (or jug) wines. The CEO thinks that there is room in the market for higher-end wine sellers and seeks to change WINECO’s focus from value wines to small, vintage wines.
Problem: How to price vintage wines There is not a long history, and demand for
small wines is more volatile. Can the CEO find a reasonable pricing
structure that the market can bear and that WINECO can profit from?
What types of promotions attract buyers without WINECO taking a huge financial hit?
127
Forecasting Objectives and Analytical Tasks Accumulate base-level transactional series to time
series data. Aggregate base level time series data to create the
wine data hierarchy. Automatically generate candidate models
for each series and select the best one as forecast specification.
Generate forecasts for each series. Assess price, holiday, and promotional
effects on selected series.
128
The Data The data is weekly case sales of wine from a wine
distribution company from January 17, 2004, to May 26, 2007.
The data hierarchy has three levels: Type (base level), Region, and Total Sales.
There are four aggregate wine types: tblred (table red), tblwt (table white), value, and vintage (limited production).
There are four regions: reg1 through reg4.
129
The Business Problem: Maximize Profit It is assumed that profit is maximized when the
following conditions are met: wine sales are maximized and inventory costs are minimized.
Wine sales are maximized when there are no lost sales due to wine being out of stock.
Inventory costs are minimized when inventories are kept as small as possible while still satisfying demand.
Accurate forecasts of wine demand over wine types and distribution regions are essential components in a profit-oriented business strategy.
130
Creating the Project for Hierarchical Forecasting
Wine Case Study
Tasks: Create the project for the WINECO data and begin the process of performing hierarchical forecasting.
131
Retail Forecasting Reconciliation Approaches
Top-down
Middle-out
Bottom-up
Company
Warehouse
Store
Company
Warehouse
Store
Warehouse
Company
Store
132
Generating Forecasts of Wine Demand
Wine Case Study
Task: Perform middle-out reconciliation of the WINECO forecasts.
133
Exercise
This exercise reinforces the concepts discussed previously.
134
Disaggregation: Forecast ProportionsReconcile Bottom to Middle
Region
Type
Value
8 4 8 12
+5 +3 +2 +3
20 25
45
135
The Reconciled Forecasts
Type
Region
Value
20
13 7
25
10 15
45
136
Reconciling Forecasts
Wine Case Study
Task: Update the forecasts in each series to account for the effect of reconciliation.
137
Assessing Price and Promotional Effects on Vintage Type Wines
Wine Case Study
Task: Interpret the parameter estimates for the final model with respect to the effects of various promotions.
138
Chapter 7: Forecasting
7.1 Introduction
7.2 Time Series Characteristics and Components
7.3 Introduction to SAS Forecast Studio
7.4 Time Series Regression Models
7.5 Time Series Data and Hierarchical Data Structure
7.6 Recommended Reading7.6 Recommended Reading
139
Recommended ReadingMay, Thornton. 2010. The New Know: Innovation Powered by Analytics. New York: Wiley. Chapters 6 through 8