Weekday Mon Tue Wed Thu Fri Mon Tue Wed Thu Fri Demand 75 80 99 108 65 80 78 104 115 68 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1. Create a forecasting model for this data, using the Winter's Seasonal Model. 2. Update this model with new information which will be given to you after you finish the first model. 140 120 100 80 60 40 20 0 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 STEP 1 STEP 2A Observations t Day -9 Monday -8 Tuesday -7 Wednesday -6 Thursday -5 Friday -4 Monday -3 Tuesday -2 Wednesday -1 Thursday 0 Friday x Centered Moving Average STEP 3 STEP2B First estimate Normalized of Seasonal Seasonal Factor Deasonalized Factor Estimate Data Average of Seasonal Factor Estimates Normalized Seasonal Factor Estimate t 75 80 99 108 65 80 78 104 115 68 85.4 86.4 86 87 88.4 89 1.159 1.250 0.756 0.920 0.882 1.169 0.925 0.887 1.171 1.257 0.760 0.925 0.887 1.171 1.257 0.760 81.10 90.15 84.58 85.91 85.51 86.51 87.90 88.85 91.48 89.46 Monday Tuesday Wednesday Thursday Friday 0.920 0.882 1.164 1.250 0.756 4.972 0.925 0.887 1.171 1.257 0.760 5.000 Weekday t Mon -9 Tue -8 Wed -7 Thu -6 Fri -5 Mon -4 Tue -3 Wed -2 Thu -1 Fri 0 -45 𝑥𝑡′ 81.10 90.15 84.58 85.91 85.51 86.51 87.90 88.85 91.48 89.46 871.44 t*x -729.893 -721.214 -592.033 -515.456 -427.558 -346.024 -263.694 -177.695 -91.4775 0 -3865.04 t2 92.00 81 64 49 36 25 16 9 4 1 0 285 y = 0.6838x + 90.221 90.00 88.00 86.00 84.00 82.00 80.00 -10 Use linear regression b0 a0 -8 -6 -4 -2 0 0.6838 90.22 Forecasts: Monday: Tuesday: 84.07 81.27 Smoothing Constants: a 0.2 b g 0.1 0.3 Monday: 89 <-- This will be given to you after completion of initialization Update: 𝑎 ෞ1 𝑏1 𝐹1 ′ 91.971 < Update the model parameters. 0.7905 0.9377 𝐹𝑡 ′ 𝐹𝑡 0.938 0.887 1.171 1.257 0.760 0.935 <-- Normalize all the factors 0.885 1.168 1.254 0.758 5.013 5.000 Forecast Tuesday: Wednesday: 82.1 109.2 Mon Tue Wed Thu Fri