Chapter 6 – Time Series Analysis & Forecasting
True / False
1. Time series methods base forecasts only on past values of the variables.
a. True
b. False
2. Quantitative forecasting methods can be used when past information about the variable being forecast is unavailable.
a. True
b. False
3. Trend in a time series must be linear.
a. True
b. False
4. All quarterly time series contain seasonality.
a. True
b. False
5. A four-period moving average forecast for period 10 would be found by averaging the values from periods 10, 9, 8, and
7.
a. True
b. False
6. If the random variability in a time series is great, a high α value should be used to exponentially smooth out the
fluctuations.
a. True
b. False
7. With fewer periods in a moving average, it will take longer to adjust to a new level of data values.
a. True
Chapter 6 – Time Series Analysis & Forecasting
b. False
8. Qualitative forecasting techniques should be applied in situations where time series data exists, but where conditions
are expected to change.
a. True
b. False
9. A time series model with a seasonal pattern will always involve quarterly data.
a. True
b. False
10. 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.
a. True
b. False
11. Smoothing methods are more appropriate for a stable time series than when significant trend or seasonal patterns are
present.
a. True
b. False
12. The exponential smoothing forecast for any period is a weighted average of all the previous actual values for the time
series.
a. True
b. False
13. The mean squared error is influenced much more by large forecast errors than is the mean absolute error.
a. True
b. False
Chapter 6 – Time Series Analysis & Forecasting
14. If a time series has a significant trend pattern, then one should not use a moving average to forecast.
a. True
b. False
15. If the random variability in a time series is great and exponential smoothing is being used to forecast, then a high
alpha (α) value should be used.
a. True
b. False
16. An alpha (α) value of .2 will cause an exponential smoothing forecast to react more quickly to a sudden drop in
demand than will an α equal to .4.
a. True
b. False
17. Exponential smoothing with α = .2 and a moving average with n = 5 put the same weight on the actual value for the
current period.
a. True
b. False
18. Time series data can exhibit seasonal patterns of less than one month in duration.
a. True
b. False
19. When using a moving average of order k to forecast, a small value for k is preferred if only the most recent values of
the time series are considered relevant.
a. True
b. False
Chapter 6 – Time Series Analysis & Forecasting
20. In situations where you need to compare forecasting methods for different time periods, relative measures such as
mean absolute error (MAE) are preferred.
a. True
b. False
Multiple Choice
21. 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.
22. 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.
23. 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.
24. 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.
25. Forecast errors
a. are the difference in successive values of a time series
b. are the differences between actual and forecast values
Chapter 6 – Time Series Analysis & Forecasting
c. should all be nonnegative
d. should be summed to judge the goodness of a forecasting model
26. To select a value for α for exponential smoothing
a. use a small α when the series varies substantially.
b. use a large α when the series has little random variability.
c. use a value between 0 and 1
d. All of the alternatives are true.
27. Linear trend is calculated as . The trend projection for period 15 is
a. 11.25
b. 28.50
c. 39.75
d. 44.25
28. 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.
29. The trend pattern is easy to identify by using
a. a moving average
b. exponential smoothing
c. regression analysis
d. a weighted moving average
30. 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
Chapter 6 – Time Series Analysis & Forecasting
31. 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
32. One measure of the accuracy of a forecasting model is the
a. smoothing constant
b. linear trend
c. mean absolute error
d. seasonal index
33. 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
34. Which of the following forecasting methods puts the least weight on the most recent time series value?
a. exponential smoothing with α = .3
b. exponential smoothing with α = .2
c. moving average using the most recent 4 periods
d. moving average using the most recent 3 periods
35. Using exponential smoothing, the demand forecast for time period 10 equals the demand forecast for time period 9
plus
a. α times (the demand forecast for time period 8)
b. α times (the error in the demand forecast for time period 9)
c. α times (the observed demand in time period 9)
d. α times (the demand forecast for time period 9)
Chapter 6 – Time Series Analysis & Forecasting
36. 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. α = .2
b. α = .25
c. α = .75
d. α = .8
37. 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.
38. 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.
39. 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
40. Whenever a categorical variable such as season has k levels, the number of dummy variables required
a. k − 1
b. k
c. k + 1
d. 2k
41. A forecasting method that computes a weighted average of all of the previous actual values of the time series is
a. exponential smoothing
Chapter 6 – Time Series Analysis & Forecasting
b. regression analysis
c. stationary average
d. weighted moving average
42. The number of cans of soft drinks sold in a machine each week is recorded below. Develop forecasts using a three-
period moving average.
338, 219, 278, 265, 314, 323, 299, 259, 287, 302
43. Use a four-period moving average to forecast attendance at baseball games. Historical records show
5346, 7812, 6513, 5783, 5982, 6519, 6283, 5577, 6712, 7345
Chapter 6 – Time Series Analysis & Forecasting
44. A hospital records the number of floral deliveries its patients receive each day. For a two-week period, the records
show
15, 27, 26, 24, 18, 21, 26, 19, 15, 28, 25, 26, 17, 23
Use exponential smoothing with a smoothing constant of .4 to forecast the number of deliveries.
45. The number of girls who attend a summer basketball camp has been recorded for the seven years the camp has been
offered. Use exponential smoothing with a smoothing constant of .8 to forecast attendance for the eighth year.
47, 68, 65, 92, 98, 121, 146
Chapter 6 – Time Series Analysis & Forecasting
46. The number of pizzas ordered on Friday evenings between 5:30 and 6:30 at a pizza delivery location for the last 10
weeks is shown below. Use exponential smoothing with smoothing constants of .2 and .8 to forecast a value for week 11.
Compare your forecasts using MSE. Which smoothing constant would you prefer?
58, 46, 55, 39, 42, 63, 54, 55, 61, 52
47. A trend line for the weekly attendance at a restaurant’s Sunday brunch is given by
How many guests would you expect in week 20?
Chapter 6 – Time Series Analysis & Forecasting
48. The number of new contributors to a public radio station’s annual fund drive over the last ten years is
63, 58, 61, 72, 98, 103, 121, 147, 163, 198
Develop a trend equation for this information, and use it to predict next year’s number of new contributors.
49. The average SAT verbal score for students from one high school over the last ten exams is
508, 490, 502, 505, 493, 506, 492, 490, 503, 501
Do the scores support an increasing or a decreasing trend?