Forecasting Methods And Applications 3rd Edition Spyros G. Makridakis, Steven C. Wheelwright, Rob J Hyndman - Solutions

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15. The Consolidated Edison Company sells electricity (82% of revenues), gas (13%), and steam (5%) in New York City and Westchester County. Bart Thomas, company forecaster, is assigned the task of forecasting the company’s quarterly revenues for the rest of 2002 and all of 2003. He collects the
14. The Triton Energy Corporation explores for and produces oil and gas. Company president Gail Freeman wants to have her company’s analyst forecast the company’s sales per share for 2000. This will be an important forecast, since Triton’s restructuring plans have hit a snag. The data are
13. Southdown, Inc., the nation’s third largest cement producer, is pushing ahead with a waste fuel burning program. The cost for Southdown will total about $37 million. For this reason, it is extremely important for the company to have an accurate forecast of revenues for the first quarter of
12. General American Investors Company, a closed-end regulated investment management company, invests primarily in medium- and high-quality stocks. Jim Campbell is studying the asset value per share for this company and would like to forecast this variable for the remaining quarters of 1996. The
11. The Hughes Supply Company uses an inventory management method to determine the monthly demands for various products.The demand values for the last 12 months of each product have been recorded and are available for future forecasting.The demand values for the 12 months of 2006 for one
10. This question refers to Problem 9. Use exponential smoothing with a smoothing constant of .2 and an initial value of 9.29 to forecast the yield for January 2007. Is this forecast better than the forecast made using the better moving average model? Explain.
9. The yield on a general obligation bond for the city of Davenport fluctuates with the market. The monthly quotations for 2006 are given in Table P-9.Month Mutual Fund Price January 19.39 February 18.96 March 18.20 April 17.89 May 18.43 June 19.98 July 19.51 August 20.63 September 19.78 October
8. Given the series in Table P-8:a. What is the forecast for period 9, using a five-period moving average?b. If simple exponential smoothing with a smoothing constant of .4 is used, what is the forecast for time period 4?c. In partb, what is the forecast error for time period 3?
7. Refer to Problem 6. Use a three-month moving average to forecast the mutual fund price for January 2007. Is this forecast better than the forecast made using the naive model? Explain.
6. Apex Mutual Fund invests primarily in technology stocks. The price of the fund at the end of each month for the 12 months of 2006 is shown in Table P-6.a. Find the forecast value of the mutual fund for each month by using a naive model (see Equation 1). The value for December 2005 was 19.00.b.
5. Which forecasting technique(s) should be tried if the data are seasonal?
4. Which forecasting technique(s) should be tried if the data are trending?
3. Which forecasting technique assigns equal weight to each observation?
2. Which forecasting technique uses the value for the current period as the forecast for the next period?
1. Which forecasting technique continually revises an estimate in the light of morerecent experiences?
2. What did Jame learn about the forecast accuracy of Karin’s suggestion TABLE 12 Monthly Sales (in kgs) for Surtido Cookies, January 2000–May 2003 Month 2000 2001 2002 2003 Jan. 666,922 802,365 574,064 1,072,617 Feb. 559,962 567,637 521,469 510,005 Mar. 441,071 527,987 522,118 579,541 Apr.
1. What should Jame conclude about the data pattern of Surtido cookie sales?
1. What did Julie conclude about the data pattern of Alomega Sales?TABLE 11 Monthly Sales for 27 Alomega Food Stores, 2003–2006 Month 2003 2004 2005 2006 Jan. 425,075 629,404 655,748 455,136 Feb. 315,305 263,467 270,483 247,570 Mar. 432,101 468,612 429,480 732,005 Apr. 357,191 313,221 260,458
3. What type of forecasting technique did Dorothy recommend for this data set?
2. What did she conclude after she completed this analysis?
1. Explain how Dorothy used autocorrelation analysis to explore the data pattern for the number of new clients seen by CCC.
4. Consider the significant autocorrelations,r12 and r24, for the differenced data.Would you conclude that the sales first differenced have a seasonal component? If so, what are the implications for forecasting, say, the monthly changes in sales? Autocorrelation 1.0- 0.8- Autocorrelation Function
3. How would you explain the line “Random 49%.”?
2. Describe the trend and seasonal effects that appear to be present in the sales data for Mr. Tux.
1. Summarize the results of John’s analysis in one paragraph that a manager, not a forecaster, can understand.
3. Which data should Julie use to develop a forecasting model
2. How does the pattern of the actual sales data compare to the pattern of the retail sales data presented in Case 1A?
1. What should Julie conclude about Murphy Brothers’ sales data?
4. How will Julie know which technique works best?
3. What forecasting techniques should Julie try?
2. Has Julie made good progress toward finding a forecasting technique?
1. What should Julie conclude about the retail sales series?
25. This question refers to Problem 23.a. Consider a naive forecasting method where the first-quarter income is used to forecast first-quarter income for the following year, second-quarter income is used to forecast second-quarter income, and so forth. For example, a forecast of first-quarter
24. This question refers to Problem 23.a. Use Minitab or Excel to compute the fourth differences of the income data in Table P-23. Fourth differences are computed by differencing observations four time periods apart. With quarterly data, this procedure is sometimes useful for creating a stationary
23. Table P-23 contains Southwest Airlines’ quarterly income before extraordinary items ($MM) for the years 1988–1999.a. Plot the income data as a time series and describe any patterns that exist.b. Is this series stationary or nonstationary? Explain.c. Using Minitab or a similar program,
22. This question refers to Problem 21.a. Fit the random model given by Equation 4 to the data in Table P-21 by estimating c with the sample mean so . Compute the residuals using .b. Using Minitab or a similar program, compute the autocorrelations of the residuals from part c for the first 10
21. Table P-21 contains the weekly sales for a food item for 52 consecutive weeks.a. Plot the sales data as a time series.b. Do you think this series is stationary or nonstationary?c. Using Minitab or a similar program, compute the autocorrelations of the sales series for the first 10 time lags. Is
20. An analyst would like to determine whether there is a pattern to earnings per share for the Price Company, which operated a wholesale/retail cash-and-carry business in several states under the name Price Club. The data are shown in Table P-20. Describe any patterns that exist in these data.a.
19. Analyze the autocorrelation coefficients for the series shown in Figures 18 through 21. Briefly describe each series.
18. This question refers to Problem 17. Compute the first differences of the quarterly loan data for Dominion Bank.a. Compute the autocorrelation coefficient for time lag 1 using the differenced data.b. Use a computer program to plot the differenced data and compute the autocorrelations for the
17. Allie White, the chief loan officer for the Dominion Bank, would like to analyze the bank’s loan portfolio for the years 2001 to 2006. The data are shown in Table P-17.a. Compute the autocorrelations for time lags 1 and 2. Test to determine whether these autocorrelation coefficients are
16. Which of the following statements is true concerning the accuracy measures used to evaluate forecasts?a. The MAPE takes into consideration the magnitude of the values being forecast.b. The MSE and RMSE penalize large errors.c. The MPE is used to determine whether a model is systematically
15. Which measure of forecast accuracy should be used in each of the following situations?a. The analyst needs to determine whether a forecasting method is biased.b. The analyst feels that the size or magnitude of the forecast variable is important in evaluating the accuracy of the forecast.c. The
14. Compute the 95% confidence interval for the autocorrelation coefficient for time lag 1 for a series that contains 80 terms.
13. The number of marriages in the United States is given in Table P-13. Compute the first differences for these data. Plot the original data and the difference data as a time series. Is there a trend in either of these series? Discuss.
12. List some of the forecasting techniques that should be considered when forecasting a cyclical series. Give examples of situations in which these techniques would be applicable.
11. List some of the forecasting techniques that should be considered when forecasting a seasonal series. Give examples of situations in which these techniques would be applicable.
10. List some of the forecasting techniques that should be considered when forecasting a trending series. Give examples of situations in which these techniques would be applicable.
9. List some of the forecasting techniques that should be considered when forecasting a stationary series. Give examples of situations in which these techniques would be applicable.
8. Descriptions are provided for several types of series: random, stationary, trending, and seasonal. Identify the type of series that each describes.a. The series has basic statistical properties, such as the mean and variance, that remain constant over time.b. The successive values of a time
7. Indicate whether each of the following statements describes a stationary or a nonstationary series.a. A series that has a trendb. A series whose mean and variance remain constant over timec. A series whose mean value is changing over timed. A series that contains no growth or decline
6. Describe how correlograms are used to analyze autocorrelations for various lags of a time series.
5. What does an autocorrelation coefficient measure?
4. What is autocorrelation?
3. Describe each of the components in a time series.
1. Explain the differences between qualitative and quantitative forecasting techniques.
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3. How easy is the package to learn and use?
1. Does the package have the facilities you want?computer platforms• 2. What platforms is the package available on?software ease-of-use
12.4 Table 12-2 in this chapter shows Armstrong's Forecasting Audit Checklist. Comment on the value of such checklist and discuss its usefulness in avoiding forecasting biases.
12.3 Where do you see the ¯eld of forecasting going in the future and what can you do to bene¯t from forthcoming changes in the environment?
12.2 Discuss the value of creating meaningful insights about the future when operating in a competitive environment where information is readily and instantly disseminated.
12.1 Describe what can and cannot be predicted while attempting to forecast the future.
10. Ability to monitor e®ectively current events and take corrective action if necessary and possible.
9. Psychological factors, market ine±ciencies, and plain accidents or mere coincidences.
8. Momentum, or inertia, that sustains established patterns and upholds existing relationships, at least in the short-term.
7. Natural events (e.g., a good or bad harvest) and their in°uence on the economic and business environment.
6. Some people's capabilities to control or slow down change (e.g., through monopolies or cartels).
5. Some people's wish to maintain the status quo.
4. Some people's ability to change the future in some favored way(e.g., through new technologies).
3. People's aspirations to change the future in some desired man-ner or direction.
2. People's preferences, tastes, and budget constraints.
1. Economic/market forces (e.g., the law of demand and supply)and biological laws (e.g., the S-shaped increases in growth) that generate and maintain various types of long-term equilibria.
11.3 The above mentioned competition includes methods that use neural networks, machine learning, and expert systems to make their predictions. Contrary to claims that such methods would outperform the more traditional methods of forecasting, this has not been the case. Comment on this result.
11.2 In the above mentioned competition we still have not com-bined the various methods to compare their performance with those of the individual methods being combined. What is your opinion about how well combining will do?
11.1 Table 11-12 shows the symmetric Mean Absolute Percent-age Error of 16 methods of the newest forecasting competi- M3-IJF Competition tion (M3-IJF) which compared 3003 time series. (The ¯rst method, NaÄ³ve Forecast 2, shows the accuracy of the 1001 series of the M-Competition.) Relate the
4. Models that minimize past errors: Available forecasting meth-ods select the best model by a process that depends upon how well a model minimizes one-step-ahead forecasting errors. How-ever, we often need forecasts for several/many periods ahead, which may not be the most appropriate ones if they
3. Unstable or changing patterns or relationships: Statistical mod-els assume that patterns and relationships are constant. This is rarely the case in the real world, however, where special events and actions, fashions, cycles, and so forth bring sys-tematic changes and therefore introduce
2. Measurement of errors: No matter what we try to measure there are always errors of measurement (including clerical and data processing errors), the size of which can be substantial and systematic. This is particularly true for disaggregate items but can be also observed on aggregate ones whose
6. Finally, regression is used most often for medium-term, followed regression by long-term, forecasting horizons. This is consistent with theoretical reasoning that postulates that in the medium- and long-terms, more emphasis should be placed on understanding the variables to be forecast and the
5. The Box-Jenkins method is not used very much for any fore- Box-Jenkins method casting horizons, which is consistent with empirical ¯ndings.
4. It is surprising that the straight-line projection method is used trend-line projection for short-term horizons. Given seasonality and cyclical factors, ups and downs in the short-term make straight-line extrapola-tions highly inaccurate. Even for the medium-term, trend-line extrapolation is not
3. Exponential smoothing and moving average methods are used exponential more for short-term, less for medium-term, and even less for smoothing long-term horizons; this is consistent with empirical evidence, which indicates that these methods perform best for shorter time horizons.
2. Sales force composites and customer expectations are used less for the long-term and more for the medium- and short-terms.Overreliance on these two methods, and on the jury of executive opinion, introduces considerable bias into forecasting. Empiri-cal ¯ndings have shown that salespeople are
1. The jury of executive opinion is the most widely used fore- judgmental forecasts casting method; furthermore, its usage is uniform across all forecasting time horizons. Although this method has some advantages, it also has some serious disadvantages, as discussed in Chapter 10.
6. Finally the satisfaction of users concerning expert systems and expert systems neural networks
5. It is somewhat surprising that classical decomposition does not classical fare better in Table 11-2. One reason might be that it is as decomposition much a tool for analysis as a forecasting method. However, its perceived value is certainly below what the empirical evidence on performance would
Table 11-2: Satisfaction with forecasting methods (as a percentage of those re-sponding). Source: Mentzer and Cox (1984).and apply while its post-sample accuracy (see below) is often not better than that of much simpler methods, such as exponen-tial smoothing. Wilson (1996) reports a higher
4. Users were the least familiar and the most dissatis¯ed with the Box-Jenkins method Box-Jenkins method. This result is consistent with empirical¯ndings indicating that the method is di±cult to understand 11/1 Surveys among forecasting users 519 Method Satis¯ed Neutral Dissatis¯ed Subjective
3. The methods of moving average and trend-line analysis also trend-line analysis produced a high level of satisfaction. Furthermore, trend-line analysis had one of the smallest percentages of dissatis¯ed users. This ¯nding is somewhat surprising in light of empirical evidence suggesting that
2. The method with which users were next most satis¯ed is ex-ponential smoothing. This ¯nding is consistent with empir- exponential ical studies reporting that exponential smoothing is capable smoothing of considerable accuracy, is easy to understand, and can be used routinely and with little
1. Regression is the method users have the highest level of satisfac- regression tion with, despite empirical ¯ndings that time series methods are more accurate than explanatory (regression and econo-metric) methods. However, since regression can be used for purposes other than simply obtaining
5. Classical decomposition is the second least familiar method. time series Only about one half of the respondents indicated any familiarity decomposition with this method, although it is one of the most useful, since it can distinguish the various subpatterns (seasonality, trend cycle, and
4. The Box-Jenkins methodology to ARIMA models is the least ARIMA models familiar of the methods included in Table 11-1. The same is true with other sophisticated methods of which practitioners are not much aware.
3. Moving average is the most familiar of the objective methods, although, from empirical studies, it is not as accurate as the method of exponential smoothing.
2. Users are also very familiar with the simpler quantitative meth-simple methods ods of moving averages, straight-line projection, exponential smoothing, and the more statistically sophisticated method of regression.
1. Forecasting users are very familiar with the subjective (judg-judgmental forecasts mental) methods of jury of executive opinion, sales force com-posite, and customer expectations.
(d) the number of predictions required.
(c) the time horizon of forecasting;

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