B D E F G H L M 1 2 3 4 5 6 7 8 Data given in below table is the count of customers entering a store each day. Management of the store believes that projecting past patterns into the future is probably a good realistic way to forecast. Goal: Develop a forecast model that forecasts for each day of week 13. You will need to decompose the data to understand the factors determining the demand pattern. Build forecasting models (and forecast week 13) using the following approaches, at a minimum: 1) exponential smoothing with Level and Trend, using alpha=0.2 and beta=0.4 2) exponential smoothing with Level and Trend, using alpha=0.4 and beta=0.2 3) exponential smoothing with Level, Trend, and Season, using alpha=0.2, beta=0.4, gamma=0.15 4) exponential smoothing with Level, Trend, and Season, using alpha-0.4, beta-0.2 gamma-0.15 For each of your models, provide at least the four measures of the error. Put each model on a separate worksheet in your Excel workbook. Discuss the results of your models, especially the fit, pointing out which one is best model and why. 9 10 11 12 13 14 15 16 17 18 19 20 Sun 21 Mon 22 Tue 23 Wed 24 Thu 25 Fri 26 Sat 27 28 29 30 31 Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 2196 2219 2256 2330 2393 2286 2404 2510 2687 2796 2879 3029 2550 2439 2514 2465 2495 2692 2608 2739 3092 3024 3148 3132 2511 2405 2518 2727 2636 2776 2776 2925 3077 3024 3168 3423 2414 2560 2559 2776 2686 2908 2858 2923 2927 3166 3213 3503 2498 2647 2681 2612 2823 2837 2831 2943 3040 3352 3233 3342 2311 2389 2486 2397 2490 2470 2490 2557 2899 2969 3207 3135 2435 2485 2439 2430 2454 2521 2507 2522 2989 3056 3170 3168