Sara manages one of the Albireds shoe lines, and is working to improve the group's forecasting capabilities so that production will more closely match actual demand (that's the goal, at least!). Work our the exponential smoothing with a smoothing constant equal to 0.2, (seed the model with a January forecast = 16,000). A weighted moving average using 0.6(t-1), 0.3(t-2) and 0.1(t-3)

Sara manages one of the Albireds shoe lines, and is working to improve the group's forecasting capabilities so that production will more closely match actual demand (that's the goal, at least!). Work our the exponential smoothing with a smoothing constant equal to 0.2, (seed the model with a January forecast = 16,000). A weighted moving average using 0.6(t-1), 0.3(t-2) and 0.1(t-3)

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section13.6: Moving Averages Models

Problem 20P: The file P13_20.xlsx contains the monthly sales of iPod cases at an electronics store for a two-year...

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The file P13_21.xlsx contains the weekly sales of rakes at a hardware store for a two-year period. Use the moving averages method, with spans of your choice, to forecast sales for the next 30 weeks. Does this method appear to track sales well? If not, what might be the reason?
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_20.xlsx contains the monthly sales of iPod cases at an electronics store for a two-year period. Use the moving averages method, with spans of your choice, to forecast sales for the next six months. Does this method appear to track sales well? If not, what might be the reason?
The file P13_27.xlsx contains yearly data on the proportion of Americans under the age of 18 living below the poverty level. 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. Create a chart of the series with the forecasts superimposed from this optimal smoothing constant. Does it make much of an improvement over the model in part b? d. Write a short report to summarize your results. Considering the chart in part c, would you say the forecasts are good?
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_01.xlsx contains the monthly number of airline tickets sold by a travel agency. a. Does a linear trend appear to fit these data well? If so, estimate and interpret the linear trend model for this time series. Also, interpret the R2 and se values. b. Provide an indication of the typical forecast error generated by the estimated model in part a. c. Is there evidence of some seasonal pattern in these sales data? If so, characterize the seasonal pattern.
The file P13_25.xlsx contains the quarterly numbers of applications for home mortgage loans at a branch office of Northern Central Bank. 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? Is it guaranteed to produce better forecasts for the future?
The following table shows quarterly sales (in thousand units) for a product over 4 years. The overall sales average for the entire 4 years is 126.0625 (thousand units).Fill in the missing values assuming there is no trend.Round seasonal indexes to 4 decimal places.Report Averages accurate to at most 2 decimal places.Report Actual sales ('000) accurate to the nearest integer. Year 1 Year 2 Year 3 Year 4 Average Seasonal Index Quarter 1 92 93 95 106 Quarter 2 105 106 105 101 Quarter 3 197 187 196 1.5211 Quarter 4 106 116 121 0.8865 PART BSuppose actual quarterly sales (in thousands) for Year 5 are 93, 123, 206, and 126 respectively. Assuming there is no trend, forecast sales for the four quarters of Year 5.Use seasonal indexes (rounded to 4 decimal places) from Part A.Report forecast answers as integers accurate to the nearest thousand.Quarter 1 forecast = thousandQuarter 2 forecast = thousandQuarter 3 forecast = thousandQuarter 4 forecast =…
The following time series shows the sales of a particular product over the past 12 months. Month Sales 1 105 2 135 3 120 4 105 5 90 6 120 7 145 8 140 9 100 10 80 11 100 12 110 (a)Use ? = 0.4 to compute the exponential smoothing values for the time series. (Round your answers to two decimal places.) Month t Time Series Value Yt Forecast Ft 1 105 2 135 3 120 4 105 5 90 6 120 7 145 8 140 9 100 10 80 11 100 12 110 (b)Use a smoothing constant of ? = 0.6 to compute the exponential smoothing forecasts. (Round your answers to two decimal places.) Month t Time Series Value Yt Forecast Ft 1 105 2 135 3 120 4 105 5 90 6 120 7 145 8 140 9 100 10 80 11 100 12 110
b. The following table shows the number of televisions sold over the last ten years at alocal electronic store.YEAR TV SALES1 1502 3003 4804 6005 6306 6407 7008 8259 90010 980i. Using trend projection, develop a formula to predict sales for years 11 and 12. Youhave to show all working. You will need to develop a table to calculate the slope andthe intercept. ii. Use that formula to forecast television sales for years 11 and 12.
Please help with correct answers in details: Step by step Suppose these data show the number of gallons of gasoline sold by a gasoline distributor in Bennington, Vermont, over the past 12 weeks. Week Sales (1,000sof gallons) 1 17 2 21 3 20 4 24 5 18 6 17 7 21 8 19 9 22 10 21 11 16 12 22 (a) Compute four-week and five-week moving averages for the time series. Week Time SeriesValue 4-WeekMovingAverageForecast 5-WeekMovingAverageForecast 1 17 2 21 3 20 4 24 5 18 6 17 7 21 8 19 9 22 10 21 11 16 12 22 b) Compute the MSE for the four-week moving average forecasts. (Round your answer to two decimal places.) c) Compute the MSE for the five-week moving average forecasts. (Round your answer to two decimal places.) d) What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? MSE for the three-week moving average is 9.65.…