Deck 15: Forecasting

Question 1

Using a naive forecasting method, the forecast for next week's sales volume equals

A) the most recent week's sales volume
B) the most recent week's forecast
C) the average of the last four weeks' sales volumes
D) next week's production volume

Answer 1

A

Question 2

If data for a time series analysis is collected on an annual basis only, which pattern does not need to be considered?

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

Answer 2

B

Question 3

All of the following are true about time series methods except

A) They discover a pattern in historical data and project it into the future.
B) They involve the use of expert judgment to develop forecasts.
C) They assume that the pattern of the past will continue into the future.
D) Their forecasts are based solely on past values of the variable or past forecast errors.

Answer 3

B

Question 4

One measure of the accuracy of a forecasting model is the

A) smoothing constant
B) linear trend
C) mean absolute error
D) seasonal index

Question 5

Seasonal patterns

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

Question 6

Which of the following exponential smoothing constant values puts the same weight on the most recent time series value as does a 5-period moving average?

A) $\alpha$ = .2
B) $\alpha$ = .25
C) $\alpha$ = .75
D) $\alpha$ = .8

Question 7

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 a value between 0 and 1
D) All of the alternatives are true.

Question 8

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

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

Question 9

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

Question 10

Gradual shifting of a time series to relatively higher or lower values over a long period of time is called

A) periodicity.
B) cycles.
C) seasonality.
D) trend.

Question 11

All of the following are true about a cyclical pattern except

A) It is often due to multiyear business cycles.
B) It is often combined with long-term trend patterns and called trend-cycle patterns.
C) It usually is easier to forecast than a seasonal pattern due to less variability.
D) It is an alternating sequence of data points above and below the trend line.

Question 12

The focus of smoothing methods is to smooth out

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

Question 13

All of the following are true about qualitative forecasting methods except

A) They generally involve the use of expert judgment to develop forecasts.
B) They assume the pattern of the past will continue into the future.
C) They are appropriate when past data on the variable being forecast are not applicable.
D) They are appropriate when past data on the variable being forecast are not available.

Question 14

In situations where you need to compare forecasting methods for different time periods, the most appropriate accuracy measure is

A) MSE
B) MAPE
C) MAE
D) ME

Question 15

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 16

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 17

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 18

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 19

All of the following are true about a stationary time series except

A) Its statistical properties are independent of time.
B) A plot of the series will always exhibit a horizontal pattern.
C) The process generating the data has a constant mean
D) There is no variability in the time series over time.

Question 20

The trend pattern is easy to identify by using

A) a moving average
B) exponential smoothing
C) regression analysis
D) a weighted moving average

Question 21

Time series data can exhibit seasonal patterns of less than one month in duration.

Question 22

All quarterly time series contain seasonality.

Question 23

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

Question 24

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 25

Trend in a time series must be linear.

Question 26

Monthly sales at a coffee shop have been analyzed. The seasonal index values are
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 27

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

Question 28

If the random variability in a time series is great and exponential smoothing is being used to forecast, then a high alpha ( $\alpha$ ) value should be used.

Question 29

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

Question 30

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

Question 31

Quantitative forecasting methods can be used when past information about the variable being forecast is unavailable.

Question 32

An alpha ( $\alpha$ ) value of .2 will cause an exponential smoothing forecast to react more quickly to a sudden drop in demand than will an $\alpha$ equal to .4.

Question 33

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

Question 34

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

Question 35

If a time series has a significant trend pattern, then one should not use a moving average to forecast.

Question 36

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

Question 37

Exponential smoothing with $\alpha$ = .2 and a moving average with n = 5 put the same weight on the actual value for the current period.

Question 38

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