Im trying to perform some time series analysis on 2015 and 2016 monthly recorded data to see what method is best for forecasting 2016 monthly values for the remainder of the year. The data has an annual cycle and I already have values for the first 9 months out of 2016. Using Winter's method, I forecasted values for the first 9 months of 2016 and summed up the absolute differences between these forecasted values and the actual 2016 values I already had. From there I calculated the 2016 monthly forecasted value to be off from the 2016 monthly actual value by a 12% average. I then tried a second method where I summed up the absolute differences between the 2015 actual monthly average 2016 actual monthly value. Long story short this elementary method resulted in predictions being off by an average of 6%. My question is how is the Winters error percentage so much higher than using a straghtforward average method. I should also note the MAPE predicted in Minitab using Winters for fitted 2015 monthly data was 2%. Can anyone make sense of all this for me?