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 |
- Prepare a time series graph of these data. What, if any, seasonal pattern do you see in the plot? Explain.
- 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.
- 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.
