The accompanying dataset provides data on the monthly usage of natural gas (in millions of cubic feet) for a certain region over two years. Implement the Holt-Winters multiplicative seasonality model with no trend to find theforecast for periods 13-26, where x = 0.5 and y = 0.8. Then find the MAD for periods 13-24.Click the icon to view the monthly use of natural gas data.Use the Holt-Winters multiplicative seasonality model with no trend to find the forecast for periods 13-18, periods 19-24, and then for periods 25 and 26.(Type integers or decimals rounded to two decimal places as needed.)ForecastPeriod131415161718Natural Gas UsageMonthJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecPeriod Gas Usage128699 A WI3457910111213141516718192021 22 23 242412211551375434292730409020122923824213634342826274086187

Holt-Winter’s multiplicative method is a forecasting technique that considers both trend and…

Answered: The accompanying dataset provides data…

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter2: Introduction To Spreadsheet Modeling

Section: Chapter Questions

Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...

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Amazon finds that sales in July are much greater relative to other months because of the Amazon Prime days in that month. Assuming that an exponential smoothing model is used for forecasting sales, what should the company best do to make the forecast for the next month (Aug) more reflective of reality? Please pick from the provided options. F t+1 = αDt + (1-α) Ft increase value of alpha decrease value of alpha stay with the same alpha decrease the expected forecast for the current month (Ft) On hearing our forecast, our clients probe - how sure are we about the forecast? Which measure of errror (among those stated below) should we best use to respond? a. Root MSE (RMSE) b. Mean absolute percentage error (MAPE) c. Mean forecast error (Avg. error) d. Cumulative forecast error (CFE)
35 The last seven weeks of demand at a new car dealer are shown below. Use a three-period weighted-moving average forecast to determine a forecast for the 8th week using weights of 3, 2, and 1 (where the most recent week receives the highest weight). (Round all forecasts to the nearest whole unit.) Calculate the MAD for this forecast (covering all weeks in which error comparisons can be made). What does the MAD indicate? Week Sales1 252 303 274 315 276 297 30
Forecasting with Linear Regression The company VID-CO-19 has manufactured swim suits for men, women, and children for 10 years. The company is located in Boston, Massachusetts. Swim suits have always had a higher demand during the months of April, May, June, July, August, and September. The demand has always been lower during the months of October, November, December, January, February, and March. TETT The real demand, based on sales of year 20X1 is the following (it starts with December of the previous year): Period Bimester Demand Season Average December-January February-March 1 100 90 2 200 180 3 April-May June-July August-September 900 910 4 3,000 2,100 4,000 4,900 October-November 300 350 VID-CO-19 has hired you as an Operations Management expert to help them to forecast demand for year 20X2 using the Linear Regression Method. What is the TOTAL "seasonalized" or "deseasonalized' forecasted demand for Quito-Ecuador in year 20X2? (write your answer rounded to two decimals)
In a retail store, the actual sales of a particular product (in thousands of units) over the past few months are as follows: Month Sales 1 16 2 22 3 18 4 20 5 23 Using exponential smoothing method with α (smoothing constant) of 0.75 and the given forecast for month 1 equal to 10, what is the forecast for month 6?
Given the following historical data, what is the simple three-period moving average forecast for period 6? Period Value 1 73 2 68 3 65 4 72 5 67
Centerville Bikes and Stuff (CBS) sells motorcycles and accessories. The number of helmets sold by CBS per week for the past six weeks follows. Week 1 2 3 Value 18 13 (a) Construct a time series plot. 20T 18 16- 14- Time Series Value O Week 1 2 3 4 5 6 What type of pattern exists in the data? O The data appear to follow a cyclical pattern. O The data appear to follow a trend pattern. 8 6 O The data appear to follow a seasonal pattern. O The data appear to follow a horizontal pattern. Week 1 2 0 1 3 4 5 6 18 13 15 12 (b) Develop the three-week moving average for this time series. (Round your answers to two decimal places.) Time Series Value 16 14 18 + 2 13 15 4 5 6 15 12 16 3 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for week 7? 12 16 4 14 Week (c) Use a = 0.2 to compute the exponential smoothing values for the time series. Time Series Value 5 14 Forecast 6 Forecast 7 20 18 16 14 12 WE 10 2 + 7 @o 20 18 0 1 2 3 4 Week 5 6 00 0 1 2 3 4 5 Week 6 7…
Based on above assume you are using the moving average forecasting method based on the last four observations. When making the forecast for the last period, theoldest of the four observations was 1945 and the forecast was 2083. Thetrue value for the last period then turned out to be 1977. Calculate yourforecast for the next period. Can you show on excel?

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