forecast of new domestic auto production mgt 267 applied business forecasting professor mohsen...
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FORECAST OF NEW DOMESTIC AUTO
PRODUCTION
MGT 267 Applied Business Forecasting
Professor Mohsen Elhafsi
Group 1:
Hui Guo
Minjia Xu
Ao Gao
Introduction
ObjectiveForecast the domestic new automobile production in the U.S. in 2015 and 2016.
MethodsHolt’s MethodMultiple RegressionTime Series DecompositionARIMA ModelCombined Model
Series – Dependent Variable
Domestic New Auto Production (De-seasonalized)
0
50
100
150
200
250
300
350
400
450
500Domestic New Auto Production
Series – Independent VariablesUrban Consumer Price Index (CPI-U)
Mar-
93
Nov-9
3
Jul-9
4
Mar-
95
Nov-9
5
Jul-9
6
Mar-
97
Nov-9
7
Jul-9
8
Mar-
99
Nov-9
9
Jul-0
0
Mar-
01
Nov-0
1
Jul-0
2
Mar-
03
Nov-0
3
Jul-0
4
Mar-
05
Nov-0
5
Jul-0
6
Mar-
07
Nov-0
7
Jul-0
8
Mar-
09
Nov-0
9
Jul-1
0
Mar-
11
Nov-1
1
Jul-1
2
Mar-
13
Nov-1
3
Jul-1
40.00
50.00
100.00
150.00
200.00
250.00
CPI-U
Series – Independent VariablesUnemployment Rate
Mar-
93
Nov-9
3
Jul-9
4
Mar-
95
Nov-9
5
Jul-9
6
Mar-
97
Nov-9
7
Jul-9
8
Mar-
99
Nov-9
9
Jul-0
0
Mar-
01
Nov-0
1
Jul-0
2
Mar-
03
Nov-0
3
Jul-0
4
Mar-
05
Nov-0
5
Jul-0
6
Mar-
07
Nov-0
7
Jul-0
8
Mar-
09
Nov-0
9
Jul-1
0
Mar-
11
Nov-1
1
Jul-1
2
Mar-
13
Nov-1
3
Jul-1
40
2
4
6
8
10
12
Unemployment Rate
Series – Independent VariablesGross Domestic Production (De-seasonalized)
Mar-
93
Nov-9
3
Jul-9
4
Mar-
95
Nov-9
5
Jul-9
6
Mar-
97
Nov-9
7
Jul-9
8
Mar-
99
Nov-9
9
Jul-0
0
Mar-
01
Nov-0
1
Jul-0
2
Mar-
03
Nov-0
3
Jul-0
4
Mar-
05
Nov-0
5
Jul-0
6
Mar-
07
Nov-0
7
Jul-0
8
Mar-
09
Nov-0
9
Jul-1
0
Mar-
11
Nov-1
1
Jul-1
2
Mar-
13
Nov-1
3
Jul-1
40.00
2000.00
4000.00
6000.00
8000.00
10000.00
12000.00
14000.00
16000.00
18000.00
Gross domestic production (GDP)
Series – Independent VariablesDisposable Personal Income (De-seasonalized)
Mar-
93
Nov-9
3
Jul-9
4
Mar-
95
Nov-9
5
Jul-9
6
Mar-
97
Nov-9
7
Jul-9
8
Mar-
99
Nov-9
9
Jul-0
0
Mar-
01
Nov-0
1
Jul-0
2
Mar-
03
Nov-0
3
Jul-0
4
Mar-
05
Nov-0
5
Jul-0
6
Mar-
07
Nov-0
7
Jul-0
8
Mar-
09
Nov-0
9
Jul-1
0
Mar-
11
Nov-1
1
Jul-1
2
Mar-
13
Nov-1
3
Jul-1
40.00
2000000.00
4000000.00
6000000.00
8000000.00
10000000.00
12000000.00
14000000.00
Disposable Personal Income (DPI)
Series – Independent VariablesInflation Rate
Mar-
93
Nov-9
3
Jul-9
4
Mar-
95
Nov-9
5
Jul-9
6
Mar-
97
Nov-9
7
Jul-9
8
Mar-
99
Nov-9
9
Jul-0
0
Mar-
01
Nov-0
1
Jul-0
2
Mar-
03
Nov-0
3
Jul-0
4
Mar-
05
Nov-0
5
Jul-0
6
Mar-
07
Nov-0
7
Jul-0
8
Mar-
09
Nov-0
9
Jul-1
0
Mar-
11
Nov-1
1
Jul-1
2
Mar-
13
Nov-1
3
Jul-1
4
-1.50
-1.00
-0.50
0.00
0.50
1.00
Infl ation rate
Series – Independent VariablesGas Price
Mar-
93
Nov-9
3
Jul-9
4
Mar-
95
Nov-9
5
Jul-9
6
Mar-
97
Nov-9
7
Jul-9
8
Mar-
99
Nov-9
9
Jul-0
0
Mar-
01
Nov-0
1
Jul-0
2
Mar-
03
Nov-0
3
Jul-0
4
Mar-
05
Nov-0
5
Jul-0
6
Mar-
07
Nov-0
7
Jul-0
8
Mar-
09
Nov-0
9
Jul-1
0
Mar-
11
Nov-1
1
Jul-1
2
Mar-
13
Nov-1
3
Jul-1
40.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
Gas price
Method – Holt’s Original series is de-seasonalized.A trend in original series.
Mar-
1993
Sep-1
993
Mar-
1994
Sep-1
994
Mar-
1995
Sep-1
995
Mar-
1996
Sep-1
996
Mar-
1997
Sep-1
997
Mar-
1998
Sep-1
998
Mar-
1999
Sep-1
999
Mar-
2000
Sep-2
000
Mar-
2001
Sep-2
001
Mar-
2002
Sep-2
002
Mar-
2003
Sep-2
003
Mar-
2004
Sep-2
004
Mar-
2005
Sep-2
005
Mar-
2006
Sep-2
006
Mar-
2007
Sep-2
007
Mar-
2008
Sep-2
008
Mar-
2009
Sep-2
009
Mar-
2010
Sep-2
010
Mar-
2011
Sep-2
011
Mar-
2012
Sep-2
012
Mar-
2013
Sep-2
013
Mar-
2014
Sep-2
014
Mar-
2015
Sep-2
015
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
500.00
New Autos
Actual Forecast Fitted Values
Method – Holt’s Accuracy
Method – Multiple Regression Suitable for all type of data
All 6 independent variables plus Time New Autos = 2,029.31+Time*14.13 + CPI * (-15.91) + Unemployment Rate * (-7.40 ) + GDP * 0.042602 + DPI * 0.000012 + Inflation * 12.32 + Gas Price*40.90
Remove Inflation and Gas PriceNew Autos = 1,079.17+Time * 7.25+ CPI * (-9.84 ) + Unemployment Rate * (-5.49 ) + GDP * 0.053008 + DPI * 0.000017
Method – Multiple Regression
Only take Unemployment Rate, DPI and Gas Price New Autos = 216.42 + Unemployment Rate * (-20.21) + DPI * 0.00004 + Gas Price * (-45.91)
Only take Unemployment Rate, GDP and Gas Price New Autos = -66.32 + Unemployment Rate * (-10.50) + Gross Domestic Product * 0.041994 + Gas Price *(-35.63)
Method – Multiple RegressionAccuracy
Method – Time Decomposition
Fits data set with trend, seasonality, and cyclical factor.
Mar-1993
Aug-1993
Jan-1994
Jun-1994
Nov-1994
Apr-1995
Sep-1995
Feb-1996
Jul-1996
Dec-1996
May-1997
Oct-1997
Mar-1998
Aug-1998
Jan-1999
Jun-1999
Nov-1999
Apr-2000
Sep-2000
Feb-2001
Jul-2001
Dec-2001
May-2002
Oct-2002
Mar-2003
Aug-2003
Jan-2004
Jun-2004
Nov-2004
Apr-2005
Sep-2005
Feb-2006
Jul-2006
Dec-2006
May-2007
Oct-2007
Mar-2008
Aug-2008
Jan-2009
Jun-2009
Nov-2009
Apr-2010
Sep-2010
Feb-2011
Jul-2011
Dec-2011
May-2012
Oct-2012
Mar-2013
Aug-2013
Jan-2014
Jun-2014
Nov-2014
0.00
100.00
200.00
300.00
400.00
500.00
600.00
New Autos
Actual Forecast Fitted Values
Holt’s Method for Cyclical Factor
Method – Time Decomposition
Fits data set with trend, seasonality, and cyclical factor.
Linear Regression for Cyclical Factor
Mar-1993
Aug-1993
Jan-1994
Jun-1994
Nov-1994
Apr-1995
Sep-1995
Feb-1996
Jul-1996
Dec-1996
May-1997
Oct-1997
Mar-1998
Aug-1998
Jan-1999
Jun-1999
Nov-1999
Apr-2000
Sep-2000
Feb-2001
Jul-2001
Dec-2001
May-2002
Oct-2002
Mar-2003
Aug-2003
Jan-2004
Jun-2004
Nov-2004
Apr-2005
Sep-2005
Feb-2006
Jul-2006
Dec-2006
May-2007
Oct-2007
Mar-2008
Aug-2008
Jan-2009
Jun-2009
Nov-2009
Apr-2010
Sep-2010
Feb-2011
Jul-2011
Dec-2011
May-2012
Oct-2012
Mar-2013
Aug-2013
Jan-2014
Jun-2014
Nov-2014
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
New Autos
Actual Forecast Fitted Values
Method – Time Decomposition
Accuracy
Method – ARIMAData set is required to be stationary.
Try different method, compare the accuracy measures, choose the best two methods.
ARIMA(p,2,q)
Mar-
93
Oct -
93
May-9
4
Dec -9
4
Jul-9
5
Feb-96
Sep-96
Apr-97
Nov-9
7
Jun-9
8
Jan -9
9
Aug-99
Mar-
00
Oct -
00
May-0
1
Dec -0
1
Jul-0
2
Feb-03
Sep-03
Apr-04
Nov-0
4
Jun-0
5
Jan -0
6
Aug-06
Mar-
07
Oct -
07
May-0
8
Dec -0
8
Jul-0
9
Feb-10
Sep-10
Apr-11
Nov-1
1
Jun-1
2
Jan -1
3
Aug-13
Mar-
14
Oct -
14
-100
-80
-60
-40
-20
0
20
40
60
80
Original data second diff erencing
Method – ARIMA
ARIMA (2,2,2)Mar-1993
Aug-1993
Jan-1994
Jun-1994
Nov-1994
Apr-1995
Sep-1995
Feb-1996
Jul-1996
Dec-1996
May-1997
Oct-1997
Mar-1998
Aug-1998
Jan-1999
Jun-1999
Nov-1999
Apr-2000
Sep-2000
Feb-2001
Jul-2001
Dec-2001
May-2002
Oct-2002
Mar-2003
Aug-2003
Jan-2004
Jun-2004
Nov-2004
Apr-2005
Sep-2005
Feb-2006
Jul-2006
Dec-2006
May-2007
Oct-2007
Mar-2008
Aug-2008
Jan-2009
Jun-2009
Nov-2009
Apr-2010
Sep-2010
Feb-2011
Jul-2011
Dec-2011
May-2012
Oct-2012
Mar-2013
Aug-2013
Jan-2014
Jun-2014
Nov-2014
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
500.00
New Autos
Actual Forecast Fitted Values
Method – ARIMAAccuracy – ARIMA(2,2,2)
Combined ModelTime Series Decomposition & ARIMA (2,2,2)
Original = -0.13517 *ARIMA+1.14 * Decomposition
Mar-1993
Aug-1993
Jan-1994
Jun-1994
Nov-1994
Apr-1995
Sep-1995
Feb-1996
Jul-1996
Dec-1996
May-1997
Oct-1997
Mar-1998
Aug-1998
Jan-1999
Jun-1999
Nov-1999
Apr-2000
Sep-2000
Feb-2001
Jul-2001
Dec-2001
May-2002
Oct-2002
Mar-2003
Aug-2003
Jan-2004
Jun-2004
Nov-2004
Apr-2005
Sep-2005
Feb-2006
Jul-2006
Dec-2006
May-2007
Oct-2007
Mar-2008
Aug-2008
Jan-2009
Jun-2009
Nov-2009
Apr-2010
Sep-2010
Feb-2011
Jul-2011
Dec-2011
May-2012
Oct-2012
Mar-2013
Aug-2013
Jan-2014
Jun-2014
Nov-2014
0.00
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
450.00
500.00
Original
Actual Forecast Fitted Values
Accuracy
Combined Model
Conclusion & Improvement
After forecasting with all methods learned in class, we chose the most prospective four to further analyze.
For our data, a combined model with Time Decomposition Holt and ARIMA provides the best results.
We could try data that are not deseasonalized to test if we can get a more accurate forecast.