**Problem 4.19**Income at the architectural firm Spraggins and Yunes for the period February to July was as follows:| Month | Income (in $ thousand) ||-------------|------------------------|| February | 70.0 || March | 68.5 || April | 64.8 || May | 71.7 || June | 71.3 || July | 72.8 |Use trend-adjusted exponential smoothing to forecast the firm’s August income. Assume that the initial forecast average for February is $65,000 and the initial trend adjustment is 0. The smoothing constants selected are $ \alpha = 0.1 $ and $ \beta = 0.2 $.**Problem 4.20**Resolve Problem 4.19 with $ \alpha = 0.1 $ and $ \beta = 0.8 $. Using Mean Squared Error (MSE), determine which smoothing constants provide a better forecast.

Formulae used: The trend-adjusted exponential forecasting is given by AFt = Ft + Tt Where, Ft =…

Answered: ...4.19 Income at the architectural…

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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9) Gasoline sales Times Series week sales (1000s of gallons 1 17 2 21 3 19 4 23 5 18 6 16 7 20 8 18 9 22 10 20 11 15 12 22 With the above gasoline time series data, show the exponential smoothing forecast using alpha = 0.1 i)Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of Alpha= 0.1 or alpha=0.2 for the gasoline sales time series? ii)Are the results the same if you apply MAE as the measure of accuracy? iii)What are the results if MAPE is used?
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.
Asvnch Problem - Statistical Forecasting Data Set – Eunice BC Fashion Monthly Sales, in million units. Year Total Sales Year Total Sales 2010 38 2016 43 2011 41 2017 40 2012 40 2018 45 2013 45 2019 47 2014 50 2020 42 2015 42 2021 48 Questions: a. Find the naïve forecast. b. Use the 3 years moving average forecast. c. Have a 5 years weighted moving average. d. Develop forecast using exponential smoothing with a = 0.2. e. Determine the trend line equation and present the forecast. f. Find the best forecast for year 2022. Note: Use the first 5 years as the training samples and the last 5 years as the forecasting samples. Solve it in Excel Sheet/Sheet with Equations as possible.
Please answer this simple question
9
a) Forecast the demand for the week of October 12 using a 3-week moving average. b) Use a 3-week weighted moving average, with weights of .1, .3, and .6, using .6 for the most recent week. Forecast demand for the week of October 12. c) compute the forecast for the week of Oct 12 using exponential smoothing with a forecast for august 31 of 360 and alpha 0.2
Income at the law firm of Smith and Jones for the period February to July was as follows: Month February March April May June July Income (in $ thousand) 90.0 91.5 96.0 85.4 92.2 96.0 Assume that the initial forecast for February is $85,000 and the initial trend adjustment is 0. The smoothing constants selected are a = 0.1 and ß = 0.2. Using trend-adjusted exponential smoothing, the forecast for the law firm's August income =O thousand dollars (round your response to two decimal places).
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?
(Use excel to show formulas used) Use regression or simple exponential smoothing with the following data. For exponential smoothing, assume the forecast for year 1 was the same as the actual and use an alpha of 0.7. Year Sales 1 290 2 340 3 400 4 410 5 400 Forecast Year 6 sales. Compute MAD.
Following table shows the weekly sales of smart phones at electronic retail store: Week Number of smart phones Sold Forecast using 3 period moving average Error Forecast using exponential smoothing (with α =0.4) Error 1 48 2 41 3 55 4 64 5 62 6 55 7 8 Answer the following questions based on the data given above. Show all your calculations. What is the expected sales for the 7th week based on 3 period moving average. What is the forecast for the 8th week using the same method if the actual sales for week 7 happens to be 70? What is the expected sales for the 7th week based on exponential smoothing with α = 0.3? Which of the above two forecasting method is better based on MSE? Explain why?

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