The following two demand sets are to be used to test two different basic exponential smoothingmodels. The first model uses 0.1, and the second uses 0.5. In both cases, the modelshould be initialized with a beginning forecast value of 50; that is, the ESF forecast for period 1made at the end of period 0 is 50 units. In each of the four cases (two models on two demandsets), compute the average forecast error and MAD. What do the results mean?Demand Set I Demand Set IIPeriod Demand Period Demand1 51 1772 46 2833 49 3904 55 4225 52 5106 47 6807 51 7168 48 8199 56 92710 51 10 7911 45 11 7312 52 12 8813 49 13 1514 48 14 2115 43 15 8516 46 16 2217 55 17 8818 53 18 7519 54 19 1420 49 20 16 10. The following two demand sets are to be used to test two different basic exponential smoothingmodels. The first model uses a = 0.1, and the second uses a = 0.5. In both cases, the modelshould be initialized with a beginning forecast value of 50; that is, the ESF forecast for period 1made at the end of period 0 is 50 units. In each of the four cases (two models on two demandsets), compute the average forecast error and MAD. What do the results mean?Demand Set IDemand Set IlPeriodDemandPeriodDemand151177246833499045542252106.4768051716848819569.271051107911451173121352128849131514481421154315851646162217551788185318751954191420492016

For each demand set, I will determine the exponential smoothing forecast using the given alpha…

10. The following two demand sets are to be used to test two different basic exponential smoothing models. The first model uses a = 0.1, and the second uses a = 0.5. In both cases, the model should be initialized with a beginning forecast value of 50; that is, the ESF forecast for period 1 made at the end of period 0 is 50 units. In each of the four cases (two models on two demand sets), compute the average forecast error and MAD. What do the results mean? Demand Set I Period 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Demand 51 46 49 55 52 47 51 48 56 51 45 52 49 48 43 46 55 53 54 49 Demand Set II Period 12345 1 2 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Demand 77 83 90 22 10 80 16 19 27 79 73 88 15 21 85 22 88 75 14 16

10. The following two demand sets are to be used to test two different basic exponential smoothing models. The first model uses a = 0.1, and the second uses a = 0.5. In both cases, the model should be initialized with a beginning forecast value of 50; that is, the ESF forecast for period 1 made at the end of period 0 is 50 units. In each of the four cases (two models on two demand sets), compute the average forecast error and MAD. What do the results mean? Demand Set I Period 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Demand 51 46 49 55 52 47 51 48 56 51 45 52 49 48 43 46 55 53 54 49 Demand Set II Period 12345 1 2 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Demand 77 83 90 22 10 80 16 19 27 79 73 88 15 21 85 22 88 75 14 16

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.7: Exponential Smoothing Models

Problem 27P: The file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living...

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The Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?
The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?
The file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.
The file P13_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?
The file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?
The file P13_28.xlsx contains monthly retail sales of U.S. liquor stores. a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?
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Do the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.