Take data, create forecast based on the classical decomposition example, except monthly data instead of quarterly.

***This is all the information I was given******

Month Demand Yearly average Seasonal Index Average Si Deseasonalized Time period 1/1/2017 2401 2/1/2017 2553 3/1/2017 2566 4/1/2017 2980 5/1/2017 3565 6/1/2017 3621 7/1/2017 2201 8/1/2017 2352.5 9/1/2017 2409.5 10/1/2017 2719 11/1/2017 3567 12/1/2017 3106 Month Demand Yearly average Seasonal Index Average Si Deseasonalized Time period 1/1/2017 2401 2/1/2017 2553 3/1/2017 2566 4/1/2017 2980 5/1/2017 3565 6/1/2017 3621 7/1/2017 2201 8/1/2017 2352.5 9/1/2017 2409.5 10/1/2017 2719 11/1/2017 3567 12/1/2017 3106