In this assignment you will apply two smoothing methods, Moving Average (MA) and Exponential Smoothing (ES), to forecast for the next period, Month 25. The actual time series data (At} represent two years of monthly sales (in $1,000's) from a line of dresses by a small clothing manufacturer. Set up your worksheet similar to that shown below. Forecasts and errors should retain 3 decimal places. In columns I-N (not shown) calculate the absolute errors for each of the 6 sets of forecasts found in columns C-H, respectively. For example, to get the absolute error in month 3 for the two-period moving average, use the formula "=ABS(B5-C5)" in cell 15. Find the Mean Absolute Deviation (MAD) for each forecasting method separately (in, say, 129:N29) using Excel's AVERAGE function. B Month Actual Sales MA (n = 2) Ft D M A TMA (n = 4) 1 (n=6) Ft Ft FTGTH ES ES ES (a = 0.3) (a = 0.5) (a = 0.7) Ft Ft 34.3 34.3 34.3 34.3 38.1 31.3 35.5 30.6 36.9 34.2 39.0 39.7 36.4 Notes: 1. Be sure to make a forecast for each method for Month 25. 43.3 2. Actual sales for Month 25 are unknown, so do not compute a forecasting error for Month 25. 50.1 37.3 39.8 32.2 38.4 37.5 39.2 378 3. For ES forecasts, Fi is an assumed value, not a true forecast, so do not include Month 1 in the MAD calculation for ES forecasts. To Do: 1. On your worksheet, circle the name of the method which performs best on this time series. 35.2 37.3 34.4 2. On your worksheet, circle the best forecast to use for Month 25. 36.0 3. Create a Line Chart of the actual sales, along with the MA(n=2) and the ES (a=0.5) forecasts. All 3 lines should be on the same chart. Make your chart big enough to be read easily. 30 4. Turn in a printout of your worksheet and your line chart