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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A large cement manufacturer uses exponential smoothing to forecast demand for a piece of pollution-control equipment. It appears that an increasing trend is present.Month At1 700 2 685 3 648 4 717 5 713 6 728 7 754 8 762 If the initial forecast for month 1 was 650 units and the trend over that period was 0 units, calculate FIT for the 9-month period. Use smoothing constants, Alpha = 0.1 andAlpha = 0.2.
Mark Gershon, owner of a musical instrument distributorship, thinks that demand for guitars may be related to the number of television appearances by the popular group Maroon 5 during the previous month. Gershon has collected the data shown in the following table: Maroon 5 TV Appearances 3 3 8 5 8 6 Demand for Guitars 2 5 6 4 11 6 This exercise contains only parts b, c, and d. b) Using the least-squares regression method, the equation for forecasting is (round your responses to four decimal places): Y = + c) The estimate for guitar sales if Maroon 5 performed on TV 11times = sales (round your response to two decimal places). d) The correlation coefficient (r)for this model = (round your response to four decimal places). The coefficient of determination (r2) for this model = (round your response to…
Since the beginning of the year, the purchase manager at a department store has been recording sales data to be used to compute 4-period moving averages to forecast sales for an upcoming month. Sales data for the months of January through September are reported in the table below. Month January February March April May June July August September Sales 21 20 39 22 29 14 42 28 27 Computed forecast values are not rounded. Compute the four-period moving average forecast for October. value as a whole number by rounding. Specify the Compute the mean absolute deviation (MAD) for the four-period moving average forecasts. Specify the MAD as a whole number by rounding. Compute the mean squared error (MSE) for the four-period moving average forecasts. Specify the MSE as a whole number by rounding. Compute the mean absolute percentage error (MAPE) for the four-period moving average forecasts. Specify the MAPE as a whole number by rounding. CHECK ANSWER
Please see attached image for chart to answer questions. Prepare a graph that shows the four-year demand history for the bow rake. Discuss any apparent trend and seasonal patterns. Which forecasting method might be best for bow rakes for each month of Year 5? Why? Use the forecasting method you recommended in part c to forecast demand for each month of Year 5. OM Explorer is highly recommended to develop your forecasts.
Develop two exponential smoothing forecasts for periods 2 through 11. For the first forecast, use a=0.1. For the second, use a=0.7. Assume that your forecast for period 1 was 205. Click the icon to view the time series data. Find the exponential smoothing forecast for each period when a=0:1 (enter your responses rounded to one decimal place). Period Forecast (0.1) 205 1 2 More Info Period 1 2 3 4 5 6 7 8 9 10 Print Demand 228 249 229 238 231 154 166 157 Demand 228 249 164 161 Done
Use the following information to compute a single exponential smoothing model forecast with a smoothing constant (α = 0.30) to produce the actual demand for the period.) Use 115 as the initial forecast. Click the icon to view the demand for the previous 9 periods. Fill-in the table below for a single exponential smoothing forecast (enter your responses rounded to one decimal place). Period F(SES) 1 2 3 4 5 6 7 8 9 The forecast for period 10 is Period 1 115 117 182 23456789 133 107 96 177 176 (Enter your response rounded to one decimal place.) 144 U at 115 117 182 133 107 96 177 176 144 forecast for period 10. (Note: a, is
Develop a forecast for the next month using a four-month moving average method. Use the Excel Functions SLOPE and INTERCEPT to write the linear regression prediction equation with Months as the independent variable and sales as the dependent variable. Use the prediction equation to estimate the number of sales in month 9.
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…
A company wants to generate a forecast for unit demand for year 2017 using exponential smoothing. The actual demand in year 2016 was 120. The forecast demand in year 2016 was 110. Using this data and a smoothing constant alpha of 0.1, which of the following is the resulting year 2017 forecast value?
After using your forecasting model for six months, you decide to test it using a tracking signal. Here are the forecast and actual demands for the six-month period: PERIOD FORECAST ACTUAL May 450 500 June 500 550 July 550 400 August 600 500 September 650 675 October 700 600 Find the tracking signal of each month
The monthly sales for Yazici Batteries, Inc., were as follows: Jan Feb Mar Apr May Jun Jul Aug Sept Oct 21 23 17 14 11 16 16 18 Nov Dec 20 20 20 24 Month Sales This exercise contains only parts b and c b) The forecast for the next month (Jan) using the naive method=sales (round your response to a whole number). The forecast for the next period (Jan) using a 3-month moving average approach = sales (round your response to two decimal places). The forecast for the next period (Jan) using a 6-month weighted average with weights of 0.10, 0.10, 0.10, 0.20, 0.20, and 0.30, where the heaviest weights are applied to the most recent month= sales (round your response to one decimal place) Using exponential smoothing with a = 0.30 and a September forecast of 21.00, the forecast for the next period (Jan) = sales (round your response to two decimal places). Using a method of trend projection, the forecast for the next month (Jan) = sales (round your response to two decimal places). c) The method…
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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