A regional supplier of jet fuel is interested in forecasting its sales. These sales data are shown for the period from 2002Q1 to 2017Q4 (data in billions of gallons):

                    Jet Fuel Sales (Billions of Gallons)

Year	Q1	Q2	Q3	Q4
2002	23.86	23.97	29.23	24.32
2003	23.89	26.84	29.36	26.30
2004	27.09	29.42	32.43	29.17
2005	28.86	32.10	34.82	30.48
2006	30.87	33.75	35.11	30.00
2007	29.95	32.63	36.78	32.34
2008	33.63	36.97	39.71	34.96
2009	35.78	38.59	42.96	39.27
2010	40.77	45.31	51.45	45.13
2011	48.13	50.35	56.73	48.83
2012	49.02	50.73	53.74	46.38
2013	46.32	51.65	52.73	47.45
2014	49.01	53.99	55.63	50.04
2015	54.77	56.89	57.82	53.30
2016	54.69	60.88	63.59	59.46
2017	61.59	68.75	71.33	64.88

1. Prepare a time series graph of these data. What, if any, seasonal pattern do you see in the plot? Explain.
2. Use Forecast X™ to make a time series decomposition (TSD) forecast for 2018. Write a brief report explaining your forecast. Include a graph of the fitted values, the forecast values, and the actual sales. 
3. Develop two other forecasts of jet fuel sales using the following methods:

Compare the MAPEs for the three models you have developed, and comment on what you like or dislike about each of the three models for this application.