Question

For this example, apply an exponental smoothing model with α = 0.25 to forecast the monthly sales for months 2 through 20


5. [20 pts] Historicall 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 α 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: 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 peniod is the absolute difference between the forecast and the observed demand for the period (ie, eFt-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. Period Demand Error (et) Error Error 0.50 | (et) α-0.25 | | α-0.30 15 23 10 32 20 a. For this example, apply an exponental smoothing model with a0.25 to forecast the monthly sales for months 2 through 20 b. Use a0.30. How do these results compare to the ones in (a)? c. Use a0.50. How do these results compare to the ones in (a) and (b)? d. Calculate the mean absolute deviation (MAD) of each model. Which value of a would you prefer for this situation? Why?

 a. For this example, apply an exponental smoothing model with α = 0.25 to forecast the monthly sales for months 2 through 20.

 b. Use α = 0.30. How do these results compare to the ones in (a)?

 c. Use α = 0.50. How do these resuts compare to the ones in (a) and (b)?

 d. Calculate the mean absolute deviation (MAD) of each model. Which value of a would you prefer for this situation? Why?


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Answer #1

a) The table is attached with the forecast value for 0.25

b) The table is attached with the forecast value for 0.3

c)The table is attached with the forecast value for 0.5

d) MAD (0.25) = 7.3955 ; MAD(0.3) = 6.6545; MAD(0.5) = 5.1582 MAD is small for Alpha value of 0.5 and which is more reliable over other two models

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