The following table contains quarterly data on Upper Midwest car sales (CS) in thousands for 1996 Q1 through 2016 Q4:
Upper Midwest Car Sales (CS)
|
Year |
Q1 |
Q2 |
Q3 |
Q4 |
|
1996 |
407.6 |
431.5 |
441.6 |
306.2 |
|
1997 |
328.7 |
381.3 |
422.6 |
369.4 |
|
1998 |
456.3 |
624.3 |
557.5 |
436.7 |
|
1999 |
485.0 |
564.3 |
538.3 |
412.5 |
|
2000 |
555.0 |
682.7 |
581.3 |
509.7 |
|
2001 |
662.7 |
591.1 |
616.9 |
529.7 |
|
2002 |
641.2 |
632.7 |
576.6 |
475.0 |
|
2003 |
542.8 |
558.9 |
581.7 |
537.8 |
|
2004 |
588.1 |
626.5 |
590.9 |
580.1 |
|
2005 |
589.2 |
643.2 |
593.9 |
612.2 |
|
2006 |
586.1 |
699.4 |
734.4 |
753.8 |
|
2007 |
691.6 |
793.4 |
864.9 |
840.8 |
|
2008 |
653.9 |
754.8 |
883.6 |
797.7 |
|
2009 |
722.2 |
788.6 |
769.9 |
725.5 |
|
2010 |
629.3 |
738.6 |
732.0 |
598.8 |
|
2011 |
603.9 |
653.6 |
606.1 |
539.7 |
|
2012 |
461.3 |
548.0 |
548.4 |
480.4 |
|
2013 |
476.6 |
528.2 |
480.4 |
452.6 |
|
2014 |
407.2 |
498.5 |
474.3 |
403.7 |
|
2015 |
418.6 |
470.2 |
470.7 |
375.7 |
|
2016 |
371.1 |
425.5 |
397.3 |
313.5 |
a. Prepare a time-series plot of Upper Midwest car sales from 1996 Q1 through 2016 Q4.
b. Use Forecast X™ to do a time-series decomposition forecast for 2017 (be sure to request the MAPE). In the results, you see the seasonal indices. Do they make sense? Why or why not?
c. Forecast X™ calculated the historic MAPE as a measure of fit. Write a short explanation of what this MAPE means to a manager.
d. Now calculate the MAPE for the 2017 Q1–2017 Q4 forecast horizon as a measure of accuracy, given that the actual values of CS for 2017 were:
|
2017 Q1 |
301.1 |
|
2017 Q2 |
336.7 |
|
2017 Q3 |
341.8 |
|
2017 Q4 |
293.5 |
e. Prepare a Winters’ exponential smoothing forecast of CS using data from 1996 Q1 through 2016 Q4 as the basis for a forecast of 2017 Q1–2017 Q4. Compare these results in terms of fit and accuracy with the results from the time-series decomposition forecast.
