1 Given the data below. Find the forecast value from June to December Month Actual Demand Forecast January February 380 420 March 375 April May 285 325 June ?? July August Sept October November ?? ?? ?? ?? ?? December ??
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- 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.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.Make a 15-year simple linear regression forecast. Example: Railroad Products Co. RPC Sales Car Loadings Year ($millions) (millions) 1 9.5 120 2 11.0 135 3 12.0 130 4 12.5 150 5 14.0 170 6 16.0 190 7 18.0 220
- a) Using the trend-projection (regression) method, develop a forecastfor the sales of Volkswagen Beetles in Nevada through year 6.b) What is the MAD?~c) What is the MSE?What does the term "adaptive forecasting" mean?A Use a simple moving average model. Experiment with the models using five weeks and three weeks past data. 3 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 5 week MA Week ATL BOS CHI DAL LA TOTAL 1 2 3 4 5 6 7 8 9 10 11 12 13 B evaluate the forecast that would have been made over the 13 weeks using overall mean absolutely deviation, mean absolute percent error, and tracking signal as criteria.
- Describe the word least-square forecasting?Compute the one-step-ahead three-month and six-month moving-average forecastsfor July through December. What effect does increasing N from 3 to 6 have on theforecasts?15 5 Two independent methods of forecasting based on judgment and experience have been prepared each month for the past 10 months. The forecasts and actual sales are as follows: 1 Month Sales Forecast 1 835 845 2 1 2 3 4 5 6 7 8 9 10 Forecast 850 820 785 840 815 840 785 800 Forecast 835 830 840 790 815 785 770 835 a. Compute the MSE and MAD for each forecast. (Round your answers to 2 decimal places.) MSE MAPE F1 MAPE F2 805 785 Answer is complete but not entirely correct. MAD 2,942.50 x Forecast 2 765 830 1,125.60 x 815 830 800 806 775 810 815 805 4.2153 X % 3.4380 % 22.50✔ Return to question 28.40✔ b. Compute MAPE for each forecast. (Round your intermediate calculations to 5 decimal places and final answers to 4 decimal places.) > Answer is complete but not entirely correct. c. Prepare a naive forecast for periods 2 through 11 using the given sales data. Compute each of the following; (1) MSE, (2) MAD, (3) tracking signal at month 10, and (4) 2s control limits. (Negative values should…
- 7. Using both time series methods of forecasting, predict the number of meals that should be prepared for the next Monday. The forecast for the most recent Monday was 367 and for the most recent Friday was 354. a=0.3 Show your calculations DAY Monday Tuesday Wednesday Thursday Friday WEEK 1 360 372 375 358 333 WEEK 2 375 381 373 368 377 WEEK 3 367 376 378 360 353 WEEK 4 373 380 376 363 370There are two general methods to forecasting:Even, what is their meaning?Here are the data for the past 21 months for actual sales of a product: Last Year This Year 275 375 350 425 400 350 350 275 350 January February March April May June July August 300 400 425 450 400 460 400 300 September 375 October 500 November 550 December 500 Develop a forecast for the fourth quarter of this year using a three-quarter, weighted moving average. Weight the most recent quarter .5, the second most recent .25, and the third .25. 1050 1500 1000 1031
