08 Aug Finding a Re-seasonalize Forecast
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 re-seasonalized forecast period 36.
Month Year Period Store F
January 1 1 50,232
February 1 2 83,681
March 1 3 104,387
April 1 4 96,752
May 1 5 83,774
June 1 6 76,779
July 1 7 79,200
August 1 8 56,070
September 1 9 52,637
October 1 10 31,947
November 1 11 48,519
December 1 12 59,693
January 2 13 63,523
February 2 14 66,227
March 2 15 94,922
April 2 16 95,126
May 2 17 83,408
June 2 18 73,902
July 2 19 83,835
August 2 20 63,282
September 2 21 41,606
October 2 22 53,142
November 2 23 54,859
December 2 24 56,785
