Deck 6: Time Series Analysis Forecasting

Question 1

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

Answer 1

True

Question 2

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

Answer 2

False

Question 3

​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.

Answer 3

True

Question 4

All quarterly time series contain seasonality.

Question 5

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

Question 6

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

Question 7

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

Question 8

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

Question 9

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.

Question 10

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

Question 11

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

Question 12

Trend in a time series must be linear.

Question 13

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.

Question 14

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

Question 15

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 16

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

Question 17

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

Question 18

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

Question 19

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

Question 20

​In situations where you need to compare forecasting methods for different time periods,relative measures such as mean absolute error (MAE)are preferred.

Question 21

A forecasting method that computes a weighted average of all of the previous actual values of the time series is ​

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

Question 22

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 23

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.

Question 24

​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

Question 25

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)

Question 26

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

Question 27

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.

Question 28

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 29

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 30

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

Question 31

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 32

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

Question 33

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 34

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 35

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 36

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 37

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 38

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

Question 39

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 40

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 41

Discuss the effects of using a small smoothing constant value and when it is most appropriate to use.Then,do the
same for a large smoothing constant value.​

Question 42

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

Question 43

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 44

Describe a time series plot and discuss its purpose and when in the forecasting process it should be constructed.​

Question 45

Explain what conditions make quantitative forecasting methods appropriate.​

Question 46

Explain and contrast three measures of forecast accuracy.​