The past sales history for Store G is provided in the table below. Using the sa

The past sales history for Store G is provided in the table below.
Using the sales provided for Years 1 and 2 (Periods 1-24) determine the seasonality index for each month. Remove seasonality from the data by dividing each period by the associated seasonality index. Forecast the sales for each month of Year 3 using time series forecasting (trend projection) on the de-seasonalized data. Then multiply the Year 3 monthly values by the seasonality index to determine the re-seasonalized forecast. Use the method for determining seasonality provided in Canvas. Do not use the method provided in Meredith. Report the MAD value for the re-seasonalized forecast.
MonthYearPeriodStore G
January1159,043
February1262,708
March1380,920
April1491,646
May 1581,929
June1672,932
July1767,709
August1857,390
September1934,612
October11035,708
November11147,713
December11255,440
January21361,104
February21470,258
March21595,627
April216107,075
May 21795,439
June21887,756
July21975,545
August22068,313
September22149,016
October22246,395
November22353,467
December22465,362
You have now moved through the next year and have the sales data available for this year:
MonthYearPeriodStore G
January32572,142
February32677,466
March32788,731
April328119,644
May 32994,898
June33075,729
July33161,078
August33253,099
September33338,947
October33431,143
November33567,784
December33675,214
Use only Year 1 and Year 2 to create the forecast, then compare that forecast to the sales for Year 3.