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

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