Deck 15: Forecasting

Question 1

Time series methods base forecasts only on past values of the variables.

Answer 1

True

Question 2

The forecasting method that is appropriate when the time series has no significant trend, cyclical, or seasonal effect is

A)moving averages
B)mean squared error
C)mean average deviation
D)qualitative forecasting methods

Answer 2

A

Question 3

The focus of smoothing methods is to smooth

A)the irregular component.
B)wide seasonal variations.
C)significant trend effects.
D)long range forecasts.

Answer 3

A

Question 4

Linear trend is calculated as T_t = 28.5 + .75t. The trend projection for period 15 is

A)11.25
B)28.50
C)39.75
D)44.25

Question 5

Quantitative forecasting methods do not require that patterns from the past will necessarily continue in the future.

Question 6

Seasonal components

A)cannot be predicted.
B)are regular repeated patterns.
C)are long runs of observations above or below the trend line.
D)reflect a shift in the series over time.

Question 7

One measure of the accuracy of a forecasting model is the

A)smoothing constant
B)trend component
C)mean absolute deviation
D)seasonal index

Question 8

A qualitative forecasting method that obtains forecasts through "group consensus" is known as the

A)Autoregressive model.
B)Delphi approach.
C)mean absolute deviation.
D)None of these alternatives is correct.

Question 9

If data for a time series analysis is collected on an annual basis only, which component may be ignored?

A)trend
B)seasonal
C)cyclical
D)irregular

Question 10

Forecast errors

A)are the difference in successive values of a time series
B)are the differences between actual and forecast values
C)should all be nonnegative
D)should be summed to judge the goodness of a forecasting model

Question 11

To select a value for $\alpha$ for exponential smoothing

A)use a small $\alpha$ when the series varies substantially.
B)use a large $\alpha$ when the series has little random variability.
C)use any value between 0 and 1
D)All of the alternatives are true.

Question 12

Time series methods

A)discover a pattern in historical data and project it into the future.
B)include cause-effect relationships.
C)are useful when historical information is not available.
D)All of the alternatives are true.

Question 13

Short-term, unanticipated, and nonrecurring factors in a time series provide the random variability known as

A)uncertainty.
B)the forecast error.
C)the residuals.
D)the irregular component.

Question 14

Gradual shifting of a time series over a long period of time is called

A)periodicity.
B)cycle.
C)regression.
D)trend.

Question 15

Which of the following is a qualitative forecasting method?

A)trend projection
B)time series method
C)smoothing method
D)Delphi method

Question 16

Using exponential smoothing, the demand forecast for time period 10 equals the demand forecast for time period 9 plus

A) $\alpha$ times (the demand forecast for time period 8)
B) $\alpha$ times (the error in the demand forecast for time period 9)
C) $\alpha$ times (the observed demand in time period 9)
D) $\alpha$ times (the demand forecast for time period 9)

Question 17

The trend component is easy to identify by using

A)moving averages
B)exponential smoothing
C)regression analysis
D)the Delphi approach

Question 18

Causal models

A)should avoid the use of regression analysis.
B)attempt to explain a time series' behavior.
C)do not use time series data.
D)All of the alternatives are true.

Question 19

Which of the following forecasting methods puts the least weight on the most recent time series value?

A)exponential smoothing with $\alpha$ = .3
B)exponential smoothing with $\alpha$ = .2
C)moving average using the most recent 4 periods
D)moving average using the most recent 3 periods

Question 20

The multiplicative model

A)uses centered moving averages to smooth the trend fluctuations.
B)removes trend before isolating the seasonal components.
C)deseasonalizes a time series by dividing the values by the appropriate seasonal index.
D)provides a unique seasonal index for each observation of the time series.

Question 21

The exponential smoothing forecast for any period is a weighted average of all the previous actual values for the time series.

Question 22

Explain what conditions make quantitative forecasting methods appropriate.

Question 23

All quarterly time series contain seasonality.

Question 24

Explain how qualitative methods frequently incorporate the opinions of multiple analysts.

Question 25

Explain how to use seasonal index values to create a forecast.

Question 26

Smoothing methods are more appropriate for a stable time series than when significant trend and/or seasonal variation are present.

Question 27

The mean squared error is influenced much more by large forecast errors than by small errors.

Question 28

If the random variability in a time series is great, a high $\alpha$ 0 value should be used to exponentially smooth out the fluctuations.

Question 29

With fewer periods in a moving average, it will take longer to adjust to a new level of demand.

Question 30

How can error measures be used to determine the number of periods to use in a moving average? What are you assuming about the future when you make this choice?

Question 31

Seasonal components with values above 1.00 indicate actual values below the trend line.

Question 32

To make period-to-period comparisons more meaningful and identify trend, the time series should be deseasonalized.

Question 33

A time series model with a seasonal component will always involve quarterly data.

Question 34

Qualitative forecasting techniques should be applied in situations where time series data exists, but where conditions are expected to change.

Question 35

Trend in a time series must be linear.

Question 36

What is a stable time series, and what forecasting methods are appropriate for one?

Question 37

A four-period moving average forecast for period 10 would be found by averaging the values from periods 10, 9, 8, and 7.

Question 38

Any recurring sequence of points above and below the trend line lasting less than one year can be attributed to the cyclical component of the time series.

Question 39

For a multiplicative time series model, the sum of the seasonal indexes should equal the number of seasons.

Question 40

A trend line for the attendance at a restaurant's Sunday brunch is given by
Number = 264 + .72(t)
How many guests would you expect in week 20?

Question 41

Quarterly billing for water usage is shown below.
a.Find the seasonal index for each quarter.
b.Deseasonalize the data.
c.Find the trend line.
d.Assume there is no cyclical component and forecast the summer billing for year 5.

Question 42

Use the following to forecast a value for period 14, a second quarter.
T = 16.32 - .18(t)
C₂ = .91
S₂ = .75

Question 43

The number of properties newly listed with a real estate agency in each quarter over the last four years is given. Calculate the seasonal index values.

Question 44

A 24-hour coffee/donut shop makes donuts every eight hours. The manager must forecast donut demand so that the bakers have the fresh ingredients they need. Listed below is the actual number of glazed donuts (in dozens) sold in each of the preceding 13 eight-hour shifts. Forecast the demand for glazed donuts for the three shifts of June 8 and the three shifts of June 9.

Question 45

In order to forecast the attendance at an annual tennis tournament, a model has been developed which uses attendance from the previous year and the amount spent for advertising this year. From the years shown in the table, forecast the attendance for years 2-5 and calculate the forecast error. The multiple regression model is Attendance = 6738 + .23($) + .25 (Attlag)

Question 46

Monthly sales at a coffee shop have been analyzed. The seasonal index values are $$\begin{array} { c r } \text { Month } & \text { Index } \\\hline \text { Jan } & 1.38 \\\text { Feb } & 1.42 \\\text { Mar } & 1.35 \\\text { Apr } & 1.03 \\\text { May } & .99 \\\text { June } & .62 \\\text { July } & .51 \\\text { Aug } & .58 \\\text { Sept } & .82 \\\text { Oct } & .82 \\\text { Nov } & .92 \\\text { Dec } & 1.56\end{array}$$ and the trend line is 74123 + 26.9(t). Assume there is no cyclical component and forecast sales for year 8 (months 97 - 108).

Question 47

A customer comment phone line is staffed from 8:00
a.Use this information to calculate a seasonal index.
a.m.to 4:30 p.m.five days a week.Records are available that show the number of calls received every day for the last five weeks.

b.Deseasonalize the data.
c.Find the trend line.
d.Assume there is no cyclical component and forecast the calls for week 6.