Help with A, B, C & D please! Help with A, B, C & D please! [20 pts.] Historical
ID: 3069923 • Letter: H
Question
Help with A, B, C & D please! Help with A, B, C & D please! [20 pts.] Historical data is often used in marketing to drive estimates of future demand. A common estimate (or forecast) used to predict future demand is the exponential smoothing. This forecasting method considers a weighted average where the most recent observations are weighted more strongly The smoothing constant a is a weighting factor and parameter of the method, and it is up to the user to fine tune it for the application of their choice. To compute an exponentially smoothed forecast in the period t +1, we use the following equation 5. In other words, the forecast for each period depends on the demand and forecast values of the previous period. Also, we assume the forecast value for period 1 is equal to the demand in period 1 The mean absolute deviation is a common criterion used to compare forecasting models. The absolute deviation for any given period is the absolute difference between the forecast and the observed demand for the period (i.e., e Fe-Del). Once an absolute deviation is calculated for every single forecasting period, they are averaged to produce the estimate of the mean absolute deviation (MAD) of the model. Error a 0.25 (e.) Error 0.50 (et) PeriodDemand a 0.30 (et) 10 16 18 13 20Explanation / Answer
MAD shall be less which is case for alpha = 0.5
Period(t) Demand
(Dt) Ft
a = 0.25
0.25(Dt)+ 0.75(Ft) Error et
(Ft-Dt) |et| Ft
a = 0.30
0.3(Dt)+ 0.7(Ft) Error et
(Ft-Dt) |et| Ft
a = 0.5
0.3(Dt)+ 0.7(Ft) Error et
(Ft-Dt) |et| 1 10 10 0 0 10 0 0 10 0 0 2 12 10 -2 2 10 -2 2 10 -2 2 3 12 10.5 -1.5 1.5 10.6 -1.4 1.4 11 -1 1 4 11 10.875 -0.125 0.125 11.02 0.02 -0.02 11.5 0.5 -0.5 5 15 10.90625 -4.09375 4.09375 11.014 -3.986 3.986 11.25 -3.75 3.75 6 16 11.92969 -4.07031 4.070313 12.2098 -3.7902 3.7902 13.125 -2.875 2.875 7 18 12.94727 -5.05273 5.052734 13.34686 -4.65314 4.65314 14.5625 -3.4375 3.4375 8 23 14.21045 -8.78955 8.789551 14.7428 -8.2572 8.257198 16.28125 -6.71875 6.71875 9 18 16.40784 -1.59216 1.592163 17.21996 -0.78004 0.780039 19.640625 1.640625 -1.64063 10 28 16.80588 -11.1941 11.19412 17.45397 -10.546 10.54603 18.8203125 -9.1796875 9.179688 11 30 19.60441 -10.3956 10.39559 20.61778 -9.38222 9.382219 23.41015625 -6.58984375 6.589844 12 31 22.20331 -8.79669 8.796694 23.43245 -7.56755 7.567553 26.70507813 -4.294921875 4.294922 13 31 24.40248 -6.59752 6.59752 25.70271 -5.29729 5.297287 28.85253906 -2.147460938 2.147461 14 37 26.05186 -10.9481 10.94814 27.2919 -9.7081 9.708101 29.92626953 -7.073730469 7.07373 15 40 28.78889 -11.2111 11.21111 30.20433 -9.79567 9.795671 33.46313477 -6.536865234 6.536865 16 32 31.59167 -0.40833 0.408329 33.14303 1.14303 -1.14303 36.73156738 4.731567383 -4.73157 17 49 31.69375 -17.3062 17.30625 32.80012 -16.1999 16.19988 34.36578369 -14.63421631 14.63422 18 46 36.02031 -9.97969 9.979685 37.66008 -8.33992 8.339915 41.68289185 -4.317108154 4.317108 19 55 38.51524 -16.4848 16.48476 40.16206 -14.8379 14.83794 43.84144592 -11.15855408 11.15855 20 60 42.63643 -17.3636 17.36357 44.61344 -15.3866 15.38656 49.42072296 -10.57927704 10.57928 Total 147.9093 130.7647 89.42072 MAD =
total/20 7.395464 6.538235 4.471036
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