Question 1

_____ is the amount by which the predicted value differs from the observed value of the time series variable.

Accepted Answer

A)  Mean forecast error 
B)  Mean absolute error 
C)  Smoothing constant 
D)  Forecast error 
A)  Mean forecast error 
B)  Mean absolute error 
C)  Smoothing constant 
D)  Forecast error D

Question 2

Qualitative forecasting methods are used when:

Accepted Answer

A)  historical data on the variable being forecast are available. 
B)  information on past values of the variable being measured is quantifiable. 
C)  historical data on the variable being forecast are either unavailable or are not applicable. 
D)  it is reasonable to assume that past is prologue. 
A)  historical data on the variable being forecast are available. 
B)  information on past values of the variable being measured is quantifiable. 
C)  historical data on the variable being forecast are either unavailable or are not applicable. 
D)  it is reasonable to assume that past is prologue. C

Question 3

A forecast is defined as a(n):

Accepted Answer

A)  prediction of future values of a time series. 
B)  quantitative method used when historical data on the variable of interest are either unavailable or not applicable. 
C)  set of observations on a variable measured at successive points in time. 
D)  outcome of a random experiment. 
A)  prediction of future values of a time series. 
B)  quantitative method used when historical data on the variable of interest are either unavailable or not applicable. 
C)  set of observations on a variable measured at successive points in time. 
D)  outcome of a random experiment. A

Question 4

The following data shows the quarterly profit (in thousands of dollars) made by a particular company in the past 3 years.
   a. Construct a time series plot. What type of pattern exists in the data?
b. Develop a three-period moving average for this time series. Compute MSE and a forecast of profit (in $1000s) for the next quarter.

Accepted Answer

Rewrite the data as below:
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a.
 @#IMG-DLM& 
The

Question 5

The below time series gives the indices of Industrial Production in U.S for 10 consecutive years.
   a. Construct a time series plot. What type of pattern exists in the data?
b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
c. What is the forecast for t = 11?

Accepted Answer

a.
 @#IMG-DLM& 
The time series plot shows a linea

Question 6

Which of the following states the objective of time series analysis?

Accepted Answer

A)  To study the variation of time with respect to increase in the variable value 
B)  To analyze the time-dependent environmental factors that affected variable values in the past 
C)  To use present variable values to study what should have been the ideal past values 
D)  To uncover a pattern in the time series and then extrapolate the pattern into the future 
A)  To study the variation of time with respect to increase in the variable value 
B)  To analyze the time-dependent environmental factors that affected variable values in the past 
C)  To use present variable values to study what should have been the ideal past values 
D)  To uncover a pattern in the time series and then extrapolate the pattern into the future

Question 7

Consider the following time series:
   a. Construct a time series plot. What type of pattern exists in the data?
b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
c. What is the forecast for t = 9?

Accepted Answer

a.
 @#IMG-DLM& 
The data appear to show a linear t

Question 8

Which of the following statements is the objective of the moving averages and exponential smoothing methods?

Accepted Answer

A)  To characterize the variable fluctuations by a smooth curve 
B)  To smooth out random fluctuations in the time series 
C)  To characterize the variable fluctuations by an exponential equation 
D)  To transform a nonstationary time series into a stationary series 
A)  To characterize the variable fluctuations by a smooth curve 
B)  To smooth out random fluctuations in the time series 
C)  To characterize the variable fluctuations by an exponential equation 
D)  To transform a nonstationary time series into a stationary series

Question 9

Which of the following data patterns best describes the scenario shown in the below plot?

Accepted Answer

A)  Time series with a linear trend pattern 
B)  Time series with a nonlinear trend pattern 
C)  Time series with no pattern 
D)  Time series with a horizontal pattern 
A)  Time series with a linear trend pattern 
B)  Time series with a nonlinear trend pattern 
C)  Time series with no pattern 
D)  Time series with a horizontal pattern

Question 10

Consider the following quarterly time series:
   a. Construct a time series plot. What type of pattern exists in the data?
b. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise.
c. Compute the quarterly forecasts for next year based on the model developed in part b.

Accepted Answer

a.
 @#IMG-DLM& 
The above time series plot reveals

Question 11

The following times series shows the demand for a particular product over the past 10 months.
   a. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for month 11.
b. Compare the three-month moving average forecast with the exponential smoothing forecast using α = 0.2. Which appears to provide the better forecast based on MSE?

Accepted Answer

a. Smoothing constant α = 0.2
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MSE = 9

Question 12

Consider the following time series:
   a. Use a multiple regression model to develop an equation to account for linear trend and seasonal effects in the data. To capture seasonal effects, use the dummy variables Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise; and create a variable t such that t = 1 for quarter 1 in year 1, t = 2 for quarter 2 in year 1, … ,t = 12 for quarter 4 in year 3.
b. Compute the quarterly forecasts for next year based on the model developed in part a.

Accepted Answer

a. Rewrite the data using dummy variable

Question 13

A time series that shows a recurring pattern over one year or less is said to follow a _____.

Accepted Answer

A)  horizontal pattern 
B)  stationary pattern 
C)  cyclical pattern 
D)  seasonal pattern 
A)  horizontal pattern 
B)  stationary pattern 
C)  cyclical pattern 
D)  seasonal pattern

Question 14

For causal modeling, _____ are used to detect linear or nonlinear relationships between the independent and dependent variables.

Accepted Answer

A)  descriptive statistics on the data 
B)  scatter charts 
C)  contingency tables 
D)  pie charts 
A)  descriptive statistics on the data 
B)  scatter charts 
C)  contingency tables 
D)  pie charts

Question 15

Which of the following is true of a stationary time series?

Accepted Answer

A)  The process generating the data has a variable mean. 
B)  The variability of the time series is constant over time. 
C)  The time series plot for this case is a straight line. 
D)  The fluctuations in values will always exhibit a cyclical pattern. 
A)  The process generating the data has a variable mean. 
B)  The variability of the time series is constant over time. 
C)  The time series plot for this case is a straight line. 
D)  The fluctuations in values will always exhibit a cyclical pattern.

Question 16

The following time series shows the sales of a particular commodity over the past 15 weeks.
   a. Construct a time series plot. What type of pattern exists in the data?
b. Use α = 0.3 to develop the exponential smoothing values for the time series and compute the forecast of demand for the next week.
c. Use trial and error to find a value of the exponential smoothing coefficient α that results in a relatively small MSE.

Accepted Answer

a.
 @#IMG-DLM& 
The time series plot shows a horiz

Question 17

With reference to time series data patterns, a cyclical pattern is the component of the time series that:

Accepted Answer

A)  shows a periodic pattern over one year or less. 
B)  does not vary with respect to time. 
C)  results in periodic above-trend and below-trend behavior of the time series lasting more than one year. 
D)  is characterized by a linear variation of the dependent variable with respect to time. 
A)  shows a periodic pattern over one year or less. 
B)  does not vary with respect to time. 
C)  results in periodic above-trend and below-trend behavior of the time series lasting more than one year. 
D)  is characterized by a linear variation of the dependent variable with respect to time.

Question 18

Consider the following time series data:
   a. Use α = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and a forecast for year 11.
b. Use trial and error to find a value of the exponential smoothing coefficient α that results in a smaller MSE than what you calculated for α = 0.2.
c. Compute the forecast for year 11 using the smoothing coefficient α selected using trial error.

Accepted Answer

a. Smoothing constant α = 0.2
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MSE = 1

Question 19

The yearly sales (in millions of dollars) of an automobile manufacturing company during the period 2000-2011 are given below:
   a. Construct a time series plot. What type of pattern exists in the data?
b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
c. What is the sales forecast (in millions of dollars) for the year 2012?

Accepted Answer

a.
 @#IMG-DLM& 
The time series plot shows a linea

Question 20

The monthly sales (in hundreds of dollars) of a company are listed below.
   a. Construct a time series plot. What type of pattern exists in the data?
b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
c. What is the sales forecast (in hundreds of dollars) for next month?

Accepted Answer

a.
 @#IMG-DLM& 
The time series plot shows a linea

_____ is the amount by which the predicted value differs from the observed value of the time series variable.

Qualitative forecasting methods are used when:

A forecast is defined as a(n):

Which of the following states the objective of time series analysis?

Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. c. What is the forecast for t = 9?

Which of the following statements is the objective of the moving averages and exponential smoothing methods?

Which of the following data patterns best describes the scenario shown in the below plot?

A time series that shows a recurring pattern over one year or less is said to follow a _____.

For causal modeling, _____ are used to detect linear or nonlinear relationships between the independent and dependent variables.

Which of the following is true of a stationary time series?

With reference to time series data patterns, a cyclical pattern is the component of the time series that:

Introduction

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Exam 5: Time Series Analysis and Forecasting

_____ is the amount by which the predicted value differs from the observed value of the time series variable.

Qualitative forecasting methods are used when:

A forecast is defined as a(n):

Which of the following states the objective of time series analysis?

Consider the following time series: a. Construct a time series plot. What type of pattern exists in the data? b. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. c. What is the forecast for t = 9?

Which of the following statements is the objective of the moving averages and exponential smoothing methods?

Which of the following data patterns best describes the scenario shown in the below plot?

A time series that shows a recurring pattern over one year or less is said to follow a _____.

For causal modeling, _____ are used to detect linear or nonlinear relationships between the independent and dependent variables.

Which of the following is true of a stationary time series?

With reference to time series data patterns, a cyclical pattern is the component of the time series that:

Introduction

Descriptive Statistics

Data Visualization

Linear Regression

Data Mining

Spreadsheet Models

Linear Optimization Models

Integer Linear Optimization Models

Nonlinear Optimization Models

Monte Carlo Simulation

Decision Analysis

Filters