Question 1

Trends result from:

Accepted Answer

A)  rapidly-arising short-term factors. 
B)  rapidly-arising long-term factors. 
C)  slowly-varying short-term factors. 
D)  slowly-varying long-term factors. 
A)  rapidly-arising short-term factors. 
B)  rapidly-arising long-term factors. 
C)  slowly-varying short-term factors. 
D)  slowly-varying long-term factors.

Question 2

Autoregressive models:

Accepted Answer

A)  use the average of the most recent data values in the time series as the forecast for the next period. 
B)  are used to smooth out random fluctuations in time series. 
C)  relate a time series to other variables that are believed to explain or cause its behavior. 
D)  occur whenever all the independent variables are previous values of the same time series. 
A)  use the average of the most recent data values in the time series as the forecast for the next period. 
B)  are used to smooth out random fluctuations in time series. 
C)  relate a time series to other variables that are believed to explain or cause its behavior. 
D)  occur whenever all the independent variables are previous values of the same time series.

Question 3

Which of the following data patterns best describes the scenario shown in the given time series plot?

Accepted Answer

A)  Linear trend pattern 
B)  Nonlinear trend pattern 
C)  Seasonal pattern 
D)  Cyclical pattern 
A)  Linear trend pattern 
B)  Nonlinear trend pattern 
C)  Seasonal pattern 
D)  Cyclical pattern

Question 4

An exponential trend pattern is appropriate when:

Accepted Answer

A)  the amount of increase between periods in the value of the variable is constant. 
B)  the percentage change between periods in the value of the variable is relatively constant. 
C)  there is a no relationship between the time series variable and time. 
D)  there are random fluctuations in the variable value with time. 
A)  the amount of increase between periods in the value of the variable is constant. 
B)  the percentage change between periods in the value of the variable is relatively constant. 
C)  there is a no relationship between the time series variable and time. 
D)  there are random fluctuations in the variable value with time.

Question 5

Using a large value for order k in the moving averages method is effective in:

Accepted Answer

A)  tracking changes in a time series more quickly. 
B)  smoothing out random fluctuations. 
C)  providing a more accurate forecast variable value. 
D)  eliminating the effect of seasonal variations in the time series. 
A)  tracking changes in a time series more quickly. 
B)  smoothing out random fluctuations. 
C)  providing a more accurate forecast variable value. 
D)  eliminating the effect of seasonal variations in the time series.

Question 6

A time series with a seasonal pattern can be modeled by treating the season as a _____.

Accepted Answer

A)  predictor variable 
B)  dependent variable 
C)  dummy variable 
D)  categorical variable 
A)  predictor variable 
B)  dependent variable 
C)  dummy variable 
D)  categorical variable

Question 7

Which of the following data patterns best describes the scenario shown in the given time series plot?

Accepted Answer

A)  Linear trend pattern 
B)  Logarithmic trend 
C)  Exponential trend 
D)  Seasonal pattern 
A)  Linear trend pattern 
B)  Logarithmic trend 
C)  Exponential trend 
D)  Seasonal pattern

Question 8

The following times series shows the demand for a particular product over the past 10 months.
   a. Construct a time series plot. What type of pattern exists in the data?
b. Develop a three-month moving average for this time series. Compute MSE and a forecast for month 11.

Accepted Answer

a.
 @#IMG-DLM& 
The data appear to follo

Question 9

The following data shows the quarterly profit (in thousands of dollars) made by a particular company in the past 3 years.
   a. Use α = 0.3 to compute the exponential smoothing values for the time series. Compute MSE and the forecast of profit (in $1000s) for the next quarter.
b. Compare the three-period moving average forecast with the exponential smoothing forecast using α = 0.3. Which appears to provide the better forecast based on MSE?

Accepted Answer

a. Smoothing constant α = 0.3
 @#IMG-DLM& 
MSE = 2

Question 10

The measures of accuracy of the forecasts:

Accepted Answer

A)  check how well a particular forecasting method is able to reproduce the time series data that are already available. 
B)  use the current value to estimate how well the model generates previous values correctly. 
C)  predict the future values and wait for a pre-defined time period to examine how accurate the predictions were. 
D)  check to see if the forecast error is negative. 
A)  check how well a particular forecasting method is able to reproduce the time series data that are already available. 
B)  use the current value to estimate how well the model generates previous values correctly. 
C)  predict the future values and wait for a pre-defined time period to examine how accurate the predictions were. 
D)  check to see if the forecast error is negative.

Question 11

Causal forecasting:

Accepted Answer

A)  does not depend upon historical values of a variable. 
B)  assumes that the variable being forecast has a cause-effect relationship with one or more other variables. 
C)  uses present variable values to study what should have been the ideal past values. 
D)  uses time series plots to study if the variable values are centered around the mean. 
A)  does not depend upon historical values of a variable. 
B)  assumes that the variable being forecast has a cause-effect relationship with one or more other variables. 
C)  uses present variable values to study what should have been the ideal past values. 
D)  uses time series plots to study if the variable values are centered around the mean.

Question 12

Consider the following time series data:
   
Using the naïve method (most recent value) as the forecast for the next year, compute the following measures of forecast accuracy: a. Mean absolute error
b. Mean squared error
c. Mean absolute percentage error
d. What is the forecast for year 11?

Accepted Answer

The following table shows the

Question 13

The monthly sales revenue (in hundreds of dollars) of a company for one year is listed below.
   a. Compute MSE using the most recent value as the forecast for the next period. What is the forecast for the next month?
b. Compute MSE using the average of all the data available as the forecast for the next period. What is the forecast for the next month?
c. Which method appears to provide the better forecast?

Accepted Answer

a.
 @#IMG-DLM& 
MSE = 42605309/11 = 3873209.91 ≈ 3

Question 14

If a time series plot exhibits a horizontal pattern, then:

Accepted Answer

A)  it is evident that the time series is stationary. 
B)  the data fluctuates randomly around a variable mean. 
C)  there is no relationship between time and the time series variable. 
D)  there is no sufficient evidence to conclude that the time series is stationary. 
A)  it is evident that the time series is stationary. 
B)  the data fluctuates randomly around a variable mean. 
C)  there is no relationship between time and the time series variable. 
D)  there is no sufficient evidence to conclude that the time series is stationary.

Question 15

Trend refers to:

Accepted Answer

A)  the long-run shift or movement in the time series observable over several periods of time. 
B)  the outcome of a random experiment. 
C)  the recurring patterns observed over successive periods of time. 
D)  the short-run shift or movement in the time series observable at some specific period of time. 
A)  the long-run shift or movement in the time series observable over several periods of time. 
B)  the outcome of a random experiment. 
C)  the recurring patterns observed over successive periods of time. 
D)  the short-run shift or movement in the time series observable at some specific period of time.

Question 16

Consider the following time series data:
   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& 
This time series plot shows an upw

Question 17

Which of the following is true of the exponential smoothing coefficient?

Accepted Answer

A)  It is a randomly generated value between -1 and +1. 
B)  It is small for a time series that has relatively little random variability. 
C)  It is chosen as the value that minimizes a selected measure of forecast accuracy such as the mean squared error. 
D)  It is computed in relation with the order value, k, for the moving averages. 
A)  It is a randomly generated value between -1 and +1. 
B)  It is small for a time series that has relatively little random variability. 
C)  It is chosen as the value that minimizes a selected measure of forecast accuracy such as the mean squared error. 
D)  It is computed in relation with the order value, k, for the moving averages.

Question 18

Which of the following measures of forecast accuracy is susceptible to the problem of positive and negative forecast errors offsetting one another?

Accepted Answer

A)  Mean absolute error 
B)  Mean forecast error 
C)  Mean squared error 
D)  Mean absolute percentage error 
A)  Mean absolute error 
B)  Mean forecast error 
C)  Mean squared error 
D)  Mean absolute percentage error

Question 19

A set of observations on a variable measured at successive points in time or over successive periods of time constitute a _____.

Accepted Answer

A)  geometric series 
B)  time invariant set 
C)  time series 
D)  logarithmic series 
A)  geometric series 
B)  time invariant set 
C)  time series 
D)  logarithmic series

Question 20

Causal models:

Accepted Answer

A)  provide evidence of a causal relationship between an independent variable and the variable to be forecast. 
B)  use the average of the most recent data values in the time series as the forecast for the next period. 
C)  occur whenever all the independent variables are previous values of the same time series. 
D)  relate a time series to other variables that are believed to explain or cause its behavior. 
A)  provide evidence of a causal relationship between an independent variable and the variable to be forecast. 
B)  use the average of the most recent data values in the time series as the forecast for the next period. 
C)  occur whenever all the independent variables are previous values of the same time series. 
D)  relate a time series to other variables that are believed to explain or cause its behavior.

Trends result from:

Autoregressive models:

Which of the following data patterns best describes the scenario shown in the given time series plot?

An exponential trend pattern is appropriate when:

Using a large value for order k in the moving averages method is effective in:

A time series with a seasonal pattern can be modeled by treating the season as a _____.

Which of the following data patterns best describes the scenario shown in the given time series plot?

The following times series shows the demand for a particular product over the past 10 months. a. Construct a time series plot. What type of pattern exists in the data? b. Develop a three-month moving average for this time series. Compute MSE and a forecast for month 11.

The measures of accuracy of the forecasts:

Causal forecasting:

Consider the following time series data: Using the naïve method (most recent value) as the forecast for the next year, compute the following measures of forecast accuracy: a. Mean absolute error b. Mean squared error c. Mean absolute percentage error d. What is the forecast for year 11?

If a time series plot exhibits a horizontal pattern, then:

Trend refers to:

Consider the following time series data: 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?

Which of the following is true of the exponential smoothing coefficient?

Which of the following measures of forecast accuracy is susceptible to the problem of positive and negative forecast errors offsetting one another?

A set of observations on a variable measured at successive points in time or over successive periods of time constitute a _____.

Causal models:

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

Exam 5: Time Series Analysis and Forecasting

Trends result from:

Autoregressive models:

Which of the following data patterns best describes the scenario shown in the given time series plot?

An exponential trend pattern is appropriate when:

Using a large value for order k in the moving averages method is effective in:

A time series with a seasonal pattern can be modeled by treating the season as a _____.

Which of the following data patterns best describes the scenario shown in the given time series plot?

The following times series shows the demand for a particular product over the past 10 months. a. Construct a time series plot. What type of pattern exists in the data? b. Develop a three-month moving average for this time series. Compute MSE and a forecast for month 11.

The measures of accuracy of the forecasts:

Causal forecasting:

Consider the following time series data: Using the naïve method (most recent value) as the forecast for the next year, compute the following measures of forecast accuracy: a. Mean absolute error b. Mean squared error c. Mean absolute percentage error d. What is the forecast for year 11?

If a time series plot exhibits a horizontal pattern, then:

Trend refers to:

Consider the following time series data: 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?

Which of the following is true of the exponential smoothing coefficient?

Which of the following measures of forecast accuracy is susceptible to the problem of positive and negative forecast errors offsetting one another?

A set of observations on a variable measured at successive points in time or over successive periods of time constitute a _____.

Causal models:

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