Although you should not expect a perfectly fitting model for any time-series data, you can consider the first differences, second differences, and percentage differences for a given series as guides in choosing an appropriate model.

Year	Series I	Series II	Series III
2005	10.0	30.0	60.0
2006	15.1	33.1	67.9
2007	24.0	36.4	76.1
2008	36.7	39.9	84.0
2009	53.8	43.9	92.2
2010	74.8	48.2	100.0
2011	100.0	53.2	108.0
2012	129.2	58.2	115.8
2013	162.4	64.5	124.1
2014	199.0	70.7	132.0
2015	239.3	77.1	140.0
2016	283.5	83.9	147.8

For this problem, use each of the time series presented in the table above and stored in TSModel1:

a. Determine the most appropriate model.

b. Compute the forecasting equation.

c. Forecast the value for 2017 .