AUTOMATIC FORECASTING SYSTEMS
HATBORO PA 19040
215-675-0652
VERSION: 07/27/2016 13:19 AFS RULES 073 alteryx R-VERSION
Time=15:29:41
Date=08/01/2016
THE MAXIMUM NUMBER OF OBSERVATIONS 10000
THE MAXIMUM NUMBER OF SERIES ALLOWED 150
THE SERIAL NUMBER IS: 10001
THE PRODUCT NUMBER IS: 6
AUTOBOX 6.09999 150 ABP10001 3 USER 3 10001
AUTOCORRELATION OF DEPENDENT SERIES. alteryx
AUTOCORRELATION FUNCTION (ACF) PARTIAL ACF
VALUE RATIO -1 0 +1 VALUE RATIO -1 0 +1
1 .915 15.82 {*}******** .915 15.82 {*}********
2 .830 8.77 { **}****** -.044 -.76 {*}
3 .739 6.35 { **}***** -.081 -1.40 {*}
4 .631 4.81 { ***}*** -.158 -2.73 *{*}
5 .554 3.93 { ***}*** .124 2.14 {*}
6 .514 3.47 { ***}** .196 3.39 {*}*
7 .533 3.46 { ***}** .364 6.29 {*}***
8 .593 3.71 { ***}*** .293 5.07 {*}**
9 .676 4.05 { ***}**** .252 4.36 {*}**
10 .745 4.23 { ***}**** -.008 -.14 {*}
11 .808 4.34 { ****}**** .129 2.23 {*}
12 .859 4.34 { ****}***** .253 4.38 {*}**
13 .787 3.75 { ****}**** -.548 -9.48 ****{*}
14 .717 3.27 { ****}*** .035 .61 {*}
15 .622 2.74 { ****}** -.197 -3.41 *{*}
16 .517 2.22 { *****} .018 .32 {*}
17 .441 1.86 { *****} -.064 -1.11 {*}
18 .398 1.66 { *****} .038 .66 {*}
19 .410 1.70 { *****} -.016 -.27 {*}
20 .466 1.91 { *****} .083 1.44 {*}
21 .537 2.18 { *****} -.077 -1.33 {*}
22 .598 2.39 { *****}* .030 .51 {*}
23 .659 2.58 { *****}** .032 .56 {*}
A HISTOGRAM OF THE PREDICTAND: alteryx
LOWER UPPER CUM-PCT #
965.0 1524. .08 24. IIIIIIIIIIIIIIIIIIIIII
1524. 2084. .20 36. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
2084. 2643. .30 29. IIIIIIIIIIIIIIIIIIIIIIIIII
2643. 3202. .41 34. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
3202. 3761. .55 42. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
3761. 4321. .72 50. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
4321. 4880. .83 33. IIIIIIIIIIIIIIIIIIIIIIIIIIIIII
4880. 5439. .92 27. IIIIIIIIIIIIIIIIIIIIIIII
5439. 5998. .95 10. IIIIIIII
5998. 6558. 1.00 13. IIIIIIIIIII
6558. 7117. 1.00 1.
THE AVERAGE IS = .350E+04
THE MEDIAN IS = .349E+04
THE STD DEV IS = .140E+04
THE MINIMUM IS = 965.
THE MAXIMUM IS = .712E+04
THE # OF VALUES= 299
DATE ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
1990/ 1 A 1172.0
1990/ 2 A 1035.0
1990/ 3 A 1732.0
1990/ 4 A 1551.0
1990/ 5 A 1598.0
1990/ 6 A 1619.0
1990/ 7 A 1823.0
1990/ 8 A 1731.0
1990/ 9 A 1356.0
1990/ 10 A 1303.0
1990/ 11 A 1303.0
1990/ 12 A 1305.0
1991/ 1 A 1103.0
1991/ 2 A 1070.0
1991/ 3 A 1201.0
1991/ 4 A 1428.0
1991/ 5 A 1779.0
1991/ 6 A 1871.0
1991/ 7 A 1582.0
1991/ 8 A 1757.0
1991/ 9 A 1444.0
1991/ 10 A 1292.0
1991/ 11 A 1184.0
1991/ 12 A 1147.0
1992/ 1 A 965.00
1992/ 2 A 1231.0
1992/ 3 A 1812.0
1992/ 4 A 1833.0
1992/ 5 A 1821.0
1992/ 6 A 1950.0
1992/ 7 A 1878.0
1992/ 8 A 1757.0
1992/ 9 A 1632.0
1992/ 10 A 1699.0
1992/ 11 A 1540.0
1992/ 12 A 1624.0
1993/ 1 A 1132.0
1993/ 2 A 1218.0
1993/ 3 A 1698.0
1993/ 4 A 1736.0
1993/ 5 A 1944.0
1993/ 6 A 2269.0
1993/ 7 A 2156.0
1993/ 8 A 2128.0
1993/ 9 A 1883.0
1993/ 10 A 1754.0
1993/ 11 A 1726.0
1993/ 12 A 1762.0
1994/ 1 A 1206.0
1994/ 2 A 1371.0
1994/ 3 A 2229.0
1994/ 4 A 2246.0
1994/ 5 A 2343.0
1994/ 6 A 2315.0
1994/ 7 A 2263.0
1994/ 8 A 2306.0
1994/ 9 A 1907.0
1994/ 10 A 1738.0
1994/ 11 A 1580.0
1994/ 12 A 1495.0
1995/ 1 A 1070.0
1995/ 2 A 1271.0
1995/ 3 A 1691.0
1995/ 4 A 1324.0
1995/ 5 A 2535.0
1995/ 6 A 2824.0
1995/ 7 A 2660.0
1995/ 8 A 2971.0
1995/ 9 A 2289.0
1995/ 10 A 2248.0
1995/ 11 A 2035.0
1995/ 12 A 2050.0
1996/ 1 A 1581.0
1996/ 2 A 2009.0
1996/ 3 A 2519.0
1996/ 4 A 2812.0
1996/ 5 A 3088.0
1996/ 6 A 2908.0
1996/ 7 A 3108.0
1996/ 8 A 2989.0
1996/ 9 A 2419.0
1996/ 10 A 2310.0
1996/ 11 A 2181.0
1996/ 12 A 2204.0
1997/ 1 A 1689.0
1997/ 2 A 2130.0
1997/ 3 A 2643.0
1997/ 4 A 2783.0
1997/ 5 A 3239.0
1997/ 6 A 3282.0
1997/ 7 A 3591.0
1997/ 8 A 3298.0
1997/ 9 A 2961.0
1997/ 10 A 3013.0
1997/ 11 A 2433.0
1997/ 12 A 2822.0
1998/ 1 A 2103.0
1998/ 2 A 2567.0
1998/ 3 A 3427.0
1998/ 4 A 3549.0
1998/ 5 A 3847.0
1998/ 6 A 4297.0
1998/ 7 A 4234.0
1998/ 8 A 3794.0
1998/ 9 A 3060.0
1998/ 10 A 3193.0
1998/ 11 A 2856.0
1998/ 12 A 3124.0
1999/ 1 A 2149.0
1999/ 2 A 2805.0
1999/ 3 A 3801.0
1999/ 4 A 3782.0
1999/ 5 A 4096.0
1999/ 6 A 4644.0
1999/ 7 A 4307.0
1999/ 8 A 4128.0
1999/ 9 A 3671.0
1999/ 10 A 3236.0
1999/ 11 A 3227.0
1999/ 12 A 3353.0
2000/ 1 A 2286.0
2000/ 2 A 3247.0
2000/ 3 A 4244.0
2000/ 4 A 3977.0
2000/ 5 A 4545.0
2000/ 6 A 4738.0
2000/ 7 A 4276.0
2000/ 8 A 4373.0
2000/ 9 A 3654.0
2000/ 10 A 3601.0
2000/ 11 A 3407.0
2000/ 12 A 3098.0
2001/ 1 A 2542.0
2001/ 2 A 3257.0
2001/ 3 A 4306.0
2001/ 4 A 3976.0
2001/ 5 A 4633.0
2001/ 6 A 5065.0
2001/ 7 A 4745.0
2001/ 8 A 4603.0
2001/ 9 A 3551.0
2001/ 10 A 3449.0
2001/ 11 A 3373.0
2001/ 12 A 3492.0
2002/ 1 A 2862.0
2002/ 2 A 3461.0
2002/ 3 A 4021.0
2002/ 4 A 4185.0
2002/ 5 A 4769.0
2002/ 6 A 4423.0
2002/ 7 A 4559.0
2002/ 8 A 4386.0
2002/ 9 A 3716.0
2002/ 10 A 3672.0
2002/ 11 A 3415.0
2002/ 12 A 3730.0
2003/ 1 A 2795.0
2003/ 2 A 2994.0
2003/ 3 A 4245.0
2003/ 4 A 4077.0
2003/ 5 A 4655.0
2003/ 6 A 4847.0
2003/ 7 A 5085.0
2003/ 8 A 5003.0
2003/ 9 A 4267.0
2003/ 10 A 4136.0
2003/ 11 A 3225.0
2003/ 12 A 3949.0
2004/ 1 A 2904.0
2004/ 2 A 3409.0
2004/ 3 A 4835.0
2004/ 4 A 5006.0
2004/ 5 A 5116.0
2004/ 6 A 5511.0
2004/ 7 A 5398.0
2004/ 8 A 5222.0
2004/ 9 A 4444.0
2004/ 10 A 4184.0
2004/ 11 A 3996.0
2004/ 12 A 4489.0
2005/ 1 A 2981.0
2005/ 2 A 3762.0
2005/ 3 A 5349.0
2005/ 4 A 5261.0
2005/ 5 A 5681.0
2005/ 6 A 6416.0
2005/ 7 A 5808.0
2005/ 8 A 6006.0
2005/ 9 A 5086.0
2005/ 10 A 4696.0
2005/ 11 A 4413.0
2005/ 12 A 4521.0
2006/ 1 A 3457.0
2006/ 2 A 4311.0
2006/ 3 A 5883.0
2006/ 4 A 5332.0
2006/ 5 A 6410.0
2006/ 6 A 7117.0
2006/ 7 A 5965.0
2006/ 8 A 6344.0
2006/ 9 A 5052.0
2006/ 10 A 5033.0
2006/ 11 A 4585.0
2006/ 12 A 4737.0
2007/ 1 A 3626.0
2007/ 2 A 4248.0
2007/ 3 A 5726.0
2007/ 4 A 5252.0
2007/ 5 A 6092.0
2007/ 6 A 6207.0
2007/ 7 A 6111.0
2007/ 8 A 6041.0
2007/ 9 A 4069.0
2007/ 10 A 4396.0
2007/ 11 A 4124.0
2007/ 12 A 3803.0
2008/ 1 A 3210.0
2008/ 2 A 3869.0
2008/ 3 A 4495.0
2008/ 4 A 4764.0
2008/ 5 A 5198.0
2008/ 6 A 5180.0
2008/ 7 A 5125.0
2008/ 8 A 4912.0
2008/ 9 A 4293.0
2008/ 10 A 3709.0
2008/ 11 A 2801.0
2008/ 12 A 3292.0
2009/ 1 A 2346.0
2009/ 2 A 2979.0
2009/ 3 A 3631.0
2009/ 4 A 3694.0
2009/ 5 A 4119.0
2009/ 6 A 4691.0
2009/ 7 A 4865.0
2009/ 8 A 4193.0
2009/ 9 A 4045.0
2009/ 10 A 4150.0
2009/ 11 A 3773.0
2009/ 12 A 3405.0
2010/ 1 A 2210.0
2010/ 2 A 2774.0
2010/ 3 A 4082.0
2010/ 4 A 4791.0
2010/ 5 A 4891.0
2010/ 6 A 4658.0
2010/ 7 A 3363.0
2010/ 8 A 3476.0
2010/ 9 A 3151.0
2010/ 10 A 2977.0
2010/ 11 A 2795.0
2010/ 12 A 3215.0
2011/ 1 A 2194.0
2011/ 2 A 2494.0
2011/ 3 A 3603.0
2011/ 4 A 3804.0
2011/ 5 A 4099.0
2011/ 6 A 4541.0
2011/ 7 A 4063.0
2011/ 8 A 4467.0
2011/ 9 A 3564.0
2011/ 10 A 3263.0
2011/ 11 A 3183.0
2011/ 12 A 3381.0
2012/ 1 A 2555.0
2012/ 2 A 3085.0
2012/ 3 A 4068.0
2012/ 4 A 4291.0
2012/ 5 A 5004.0
2012/ 6 A 5196.0
2012/ 7 A 4859.0
2012/ 8 A 5264.0
2012/ 9 A 4151.0
2012/ 10 A 4214.0
2012/ 11 A 3875.0
2012/ 12 A 3849.0
2013/ 1 A 3190.0
2013/ 2 A 3557.0
2013/ 3 A 5041.0
2013/ 4 A 5545.0
2013/ 5 A 6159.0
2013/ 6 A 5981.0
2013/ 7 A 6322.0
2013/ 8 A 6209.0
2013/ 9 A 4941.0
2013/ 10 A 4593.0
2013/ 11 A 3964.0
2013/ 12 A 4212.0
2014/ 1 A 3152.0
2014/ 2 A 3769.0
2014/ 3 A 4730.0
2014/ 4 A 5165.0
2014/ 5 A 5924.0
2014/ 6 A 6309.0
2014/ 7 A 6256.0
2014/ 8 A 5925.0
2014/ 9 A 5138.0
2014/ 10 A 5099.0
2014/ 11 A 3959.0
++++++++++++++++++++++++++++++++++++++++++++++++++++++++
To do science is to search for repeated patterns.
To detect anomalies is to identify values that do not follow repeated patterns.
For whoever knows the ways of Nature will more easily notice her deviations
and, on the other hand, whoever knows her deviations will more accurately
describe her ways.
One learns the rules by observing when the current rules fail.
AUTOBOX WILL ANSWER THE FOLLOWING QUESTION
Can you tell me the probability that a single data point (e.g. the latest
reading) came from the distribution represented by all the previous data points?
Analysis for Variable Y alteryx
LAG ACF STND. T- CHI-SQUARE & PACF STND. T-
VALUE ERROR RATIO PROBABILITY VALUE ERROR RATIO
1 .915 .058 15.82 252.8 .0000 .915 .058 15.82
2 .830 .095 8.77 461.4 .0000 -.044 .058 -.76
3 .739 .116 6.35 627.5 .0000 -.081 .058 -1.40
4 .631 .131 4.81 749.2 .0000 -.158 .058 -2.73
5 .554 .141 3.93 843.1 .0000 .124 .058 2.14
6 .514 .148 3.47 924.2 .0000 .196 .058 3.39
7 .533 .154 3.46 1011.6 .0000 .364 .058 6.29
8 .593 .160 3.71 1120.2 .0000 .293 .058 5.07
9 .676 .167 4.05 1262.1 .0000 .252 .058 4.36
10 .745 .176 4.23 1434.8 .0000 -.008 .058 -.14
11 .808 .186 4.34 1638.8 .0000 .129 .058 2.23
12 .859 .198 4.34 1870.1 .0000 .253 .058 4.38
13 .787 .210 3.75 2065.0 .0000 -.548 .058 -9.48
14 .717 .219 3.27 2227.5 .0000 .035 .058 .61
15 .622 .227 2.74 2350.0 .0000 -.197 .058 -3.41
16 .517 .233 2.22 2435.0 .0000 .018 .058 .32
17 .441 .237 1.86 2497.1 .0000 -.064 .058 -1.11
18 .398 .239 1.66 2547.9 .0000 .038 .058 .66
19 .410 .242 1.70 2602.0 .0000 -.016 .058 -.27
20 .466 .244 1.91 2672.1 .0000 .083 .058 1.44
21 .537 .247 2.18 2765.4 .0000 -.077 .058 -1.33
22 .598 .251 2.39 2881.7 .0000 .030 .058 .51
23 .659 .255 2.58 3023.2 .0000 .032 .058 .56
24 .699 .261 2.68 3183.0 .0000 .077 .058 1.33
AUTOCORRELATION FUNCTION (ACF) PARTIAL ACF
VALUE RATIO -1 0 +1 VALUE RATIO -1 0 +1
1 .915 15.82 {*}******** .915 15.82 {*}********
2 .830 8.77 { **}****** -.044 -.76 {*}
3 .739 6.35 { **}***** -.081 -1.40 {*}
4 .631 4.81 { ***}*** -.158 -2.73 *{*}
5 .554 3.93 { ***}*** .124 2.14 {*}
6 .514 3.47 { ***}** .196 3.39 {*}*
7 .533 3.46 { ***}** .364 6.29 {*}***
8 .593 3.71 { ***}*** .293 5.07 {*}**
9 .676 4.05 { ***}**** .252 4.36 {*}**
10 .745 4.23 { ***}**** -.008 -.14 {*}
11 .808 4.34 { ****}**** .129 2.23 {*}
12 .859 4.34 { ****}***** .253 4.38 {*}**
13 .787 3.75 { ****}**** -.548 -9.48 ****{*}
14 .717 3.27 { ****}*** .035 .61 {*}
15 .622 2.74 { ****}** -.197 -3.41 *{*}
16 .517 2.22 { *****} .018 .32 {*}
17 .441 1.86 { *****} -.064 -1.11 {*}
18 .398 1.66 { *****} .038 .66 {*}
19 .410 1.70 { *****} -.016 -.27 {*}
20 .466 1.91 { *****} .083 1.44 {*}
21 .537 2.18 { *****} -.077 -1.33 {*}
22 .598 2.39 { *****}* .030 .51 {*}
WE NOW DIFFERENCE THE DATA
AUTOCORRELATION FUNCTION (ACF) PARTIAL ACF
VALUE RATIO -1 0 +1 VALUE RATIO -1 0 +1
1 -.211 -3.57 *{*} -.211 -3.65 *{*}
2 -.230 -3.72 *{*} -.287 -4.96 **{*}
3 .067 1.04 {*} -.064 -1.10 {*}
4 -.112 -1.73 {*} -.202 -3.49 *{*}
5 .029 .45 {*} -.061 -1.05 {*}
6 .035 .53 {*} -.062 -1.07 {*}
7 -.025 -.39 {*} -.042 -.73 {*}
8 -.035 -.53 {*} -.087 -1.51 {*}
9 .127 1.93 {*} .097 1.68 {*}
10 .011 .16 {*} .054 .93 {*}
11 .069 1.03 {*} .186 3.22 {*}*
12 -.328 -4.91 **{*} -.303 -5.24 **{*}
13 -.132 -1.82 {*} -.270 -4.68 **{*}
14 .317 4.34 {*}** .038 .66 {*}
15 -.001 -.01 { * } .039 .67 {*}
16 -.110 -1.42 {** } -.128 -2.21 {*}
17 .087 1.11 { **} -.001 -.01 {*}
18 .012 .15 { * } .051 .89 {*}
19 -.146 -1.85 {** } -.131 -2.27 {*}
20 .158 1.98 { **} .039 .68 {*}
21 .033 .41 { * } .166 2.86 {*}*
22 -.249 -3.09 {** } -.132 -2.28 {*}
LAG ACF STND. T- CHI-SQUARE & PACF STND. T-
VALUE ERROR RATIO PROBABILITY VALUE ERROR RATIO
1 -.211 .059 -3.57 12.9 .0003 -.211 .058 -3.65
2 -.230 .062 -3.72 28.2 .0000 -.287 .058 -4.96
3 .067 .065 1.04 29.5 .0000 -.064 .058 -1.10
4 -.112 .065 -1.73 33.2 .0000 -.202 .058 -3.49
5 .029 .066 .45 33.4 .0000 -.061 .058 -1.05
6 .035 .066 .53 33.8 .0000 -.062 .058 -1.07
7 -.025 .066 -.39 34.0 .0000 -.042 .058 -.73
8 -.035 .066 -.53 34.3 .0000 -.087 .058 -1.51
9 .127 .066 1.93 39.1 .0000 .097 .058 1.68
10 .011 .067 .16 39.2 .0000 .054 .058 .93
11 .069 .067 1.03 40.6 .0000 .186 .058 3.22
12 -.328 .067 -4.91 72.9 .0000 -.303 .058 -5.24
13 -.132 .072 -1.82 78.1 .0000 -.270 .058 -4.68
14 .317 .073 4.34 108.5 .0000 .038 .058 .66
15 -.001 .078 -.01 108.5 .0000 .039 .058 .67
16 -.110 .078 -1.42 112.3 .0000 -.128 .058 -2.21
17 .087 .078 1.11 114.6 .0000 -.001 .058 -.01
18 .012 .079 .15 114.6 .0000 .051 .058 .89
19 -.146 .079 -1.85 121.1 .0000 -.131 .058 -2.27
20 .158 .080 1.98 128.9 .0000 .039 .058 .68
21 .033 .081 .41 129.2 .0000 .166 .058 2.86
22 -.249 .081 -3.09 148.6 .0000 -.132 .058 -2.28
23 .223 .083 2.67 164.1 .0000 .170 .058 2.94
24 -.027 .085 -.32 164.4 .0000 -.145 .058 -2.50
ADDED ARIMA MODEL STRUCTURE
THE INITIAL MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1CONSTANT .100
2Autoregressive-Factor # 1 1 -.272
3 2 -.287
4Autoregressive-Factor # 2 12 -.328
MODEL STAGE: 7 1EST 1
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 268
Residual Mean =Sum R / n .000000
Error/Residual Sum of Squares =Sum R**2 .282959E+08
Variance =SOS/(n) 94635.0
Adjusted Variance =SOS/(n-m) 105582.
Standard Deviation RMSE =SQRT(Adj Var) 324.933
Standard Error of the Mean =Standard Dev/ (n-m) 19.8485
Mean / its Standard Error =Mean/SEM .000000
Mean Absolute Deviation =Sum(ABS(R))/n 247.060
AIC Value ( Uses var ) =nln +2m 11.4845
SBC Value ( Uses var ) =nln +m*lnn 3138.94
BIC Value ( Uses var ) =see Wei p153 11.5340
R Square = .937337
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98364
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
THE INITIAL MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1CONSTANT -.188E-01 19.6 .9992 .00
2Autoregressive-Factor # 1 1 -.385 .589E-01 .0000 -6.53
3 2 -.266 .589E-01 .0000 -4.52
4Autoregressive-Factor # 2 12 -.397 .565E-01 .0000 -7.03
MODEL STAGE: 12 2EST 1
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 268
Residual Mean =Sum R / n .000000
Error/Residual Sum of Squares =Sum R**2 .282959E+08
Variance =SOS/(n) 94635.0
Adjusted Variance =SOS/(n-m) 105582.
Standard Deviation RMSE =SQRT(Adj Var) 324.933
Standard Error of the Mean =Standard Dev/ (n-m) 19.8485
Mean / its Standard Error =Mean/SEM .000000
Mean Absolute Deviation =Sum(ABS(R))/n 247.054
AIC Value ( Uses var ) =nln +2m 11.4845
SBC Value ( Uses var ) =nln +m*lnn 3138.94
BIC Value ( Uses var ) =see Wei p153 11.5340
R Square = .937337
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98304
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
THE INITIAL MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1CONSTANT -.181E-01 19.6 .9993 .00
2Autoregressive-Factor # 1 1 -.385 .589E-01 .0000 -6.54
3 2 -.266 .589E-01 .0000 -4.51
4Autoregressive-Factor # 2 12 -.397 .565E-01 .0000 -7.03
THE INITIAL MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1CONSTANT -.181E-01 19.6 .9993 .00
2Autoregressive-Factor # 1 1 -.385 .589E-01 .0000 -6.54
3 2 -.266 .589E-01 .0000 -4.51
4Autoregressive-Factor # 2 12 -.397 .565E-01 .0000 -7.03
LAG ACF STND. T- CHI-SQUARE & PACF STND. T-
VALUE ERROR RATIO PROBABILITY VALUE ERROR RATIO
1 .005 .061 .08 .0 NA .005 .058 .08
2 -.057 .061 -.94 .9 NA -.057 .058 -.99
3 -.064 .061 -1.05 2.0 NA -.063 .058 -1.10
4 -.232 .061 -3.79 17.0 NA -.236 .058 -4.09
5 .034 .064 .54 17.3 .0000 .026 .058 .45
6 .017 .064 .27 17.4 .0002 -.017 .058 -.29
7 -.005 .064 -.08 17.4 .0006 -.033 .058 -.57
8 -.014 .064 -.22 17.5 .0016 -.070 .058 -1.21
9 .175 .064 2.72 26.1 .0001 .200 .058 3.45
10 -.125 .066 -1.90 30.6 .0000 -.149 .058 -2.58
11 .013 .067 .19 30.6 .0001 .033 .058 .57
12 -.060 .067 -.90 31.7 .0001 -.086 .058 -1.49
13 -.187 .067 -2.79 41.8 .0000 -.115 .058 -1.99
14 .294 .069 4.25 66.7 .0000 .242 .058 4.19
15 .034 .073 .46 67.0 .0000 .011 .058 .19
16 -.088 .074 -1.19 69.2 .0000 -.130 .058 -2.24
17 -.012 .074 -.16 69.3 .0000 -.015 .058 -.26
18 -.043 .074 -.58 69.8 .0000 .050 .058 .87
19 -.078 .074 -1.05 71.6 .0000 -.087 .058 -1.50
20 .159 .074 2.15 79.1 .0000 .122 .058 2.12
21 .061 .076 .80 80.2 .0000 .072 .058 1.24
22 -.191 .076 -2.52 91.1 .0000 -.186 .058 -3.22
23 .230 .077 2.97 107.0 .0000 .180 .058 3.12
24 -.231 .080 -2.89 123.0 .0000 -.207 .058 -3.58
AUTOBOX WILL NOW ANALYZE THE RESIDUALS IN ORDER TO ASSESS THE SUFFICIENCY AND
TO DETERMINE POSSIBLE MODEL IMPROVEMENTS.
DIAGNOSTIC CHECK #2: THE SUFFICIENCY TEST
AUTOCORRELATION FUNCTION (ACF) PARTIAL ACF
VALUE RATIO -1 0 +1 VALUE RATIO -1 0 +1
1 .005 .08 {*} .005 .08 {*}
2 -.057 -.94 {*} -.057 -.99 {*}
3 -.064 -1.05 {*} -.063 -1.10 {*}
4 -.232 -3.79 *{*} -.236 -4.09 *{*}
5 .034 .54 {*} .026 .45 {*}
6 .017 .27 {*} -.017 -.29 {*}
7 -.005 -.08 {*} -.033 -.57 {*}
8 -.014 -.22 {*} -.070 -1.21 {*}
9 .175 2.72 {*}* .200 3.45 {*}*
10 -.125 -1.90 {*} -.149 -2.58 {*}
11 .013 .19 {*} .033 .57 {*}
12 -.060 -.90 {*} -.086 -1.49 {*}
13 -.187 -2.79 *{*} -.115 -1.99 {*}
14 .294 4.25 {*}** .242 4.19 {*}*
15 .034 .46 {*} .011 .19 {*}
16 -.088 -1.19 {*} -.130 -2.24 {*}
17 -.012 -.16 {*} -.015 -.26 {*}
18 -.043 -.58 {*} .050 .87 {*}
19 -.078 -1.05 {*} -.087 -1.50 {*}
20 .159 2.15 {*}* .122 2.12 {*}
21 .061 .80 {*} .072 1.24 {*}
22 -.191 -2.52 *{*} -.186 -3.22 *{*}
23 .230 2.97 {*}* .180 3.12 {*}*
24 -.231 -2.89 *{*} -.207 -3.58 *{*}
MODEL STAGE: 12 3EST 2
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 269
Residual Mean =Sum R / n -.175990E-01
Error/Residual Sum of Squares =Sum R**2 .282959E+08
Variance =SOS/(n) 94635.0
Adjusted Variance =SOS/(n-m) 105189.
Standard Deviation RMSE =SQRT(Adj Var) 324.329
Standard Error of the Mean =Standard Dev/ (n-m) 19.7747
Mean / its Standard Error =Mean/SEM -.889979E-03
Mean Absolute Deviation =Sum(ABS(R))/n 247.054
AIC Value ( Uses var ) =nln +2m 11.4778
SBC Value ( Uses var ) =nln +m*lnn 3133.33
BIC Value ( Uses var ) =see Wei p153 11.5150
R Square = .937337
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98299
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.385 .589E-01 .0000 -6.54
2 2 -.266 .589E-01 .0000 -4.51
3Autoregressive-Factor # 2 12 -.397 .564E-01 .0000 -7.03
MODEL STAGE: 33 4EST 3
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 269
Residual Mean =Sum R / n -.175414E-01
Error/Residual Sum of Squares =Sum R**2 .282959E+08
Variance =SOS/(n) 94635.0
Adjusted Variance =SOS/(n-m) 105189.
Standard Deviation RMSE =SQRT(Adj Var) 324.329
Standard Error of the Mean =Standard Dev/ (n-m) 19.7747
Mean / its Standard Error =Mean/SEM -.887064E-03
Mean Absolute Deviation =Sum(ABS(R))/n 247.053
AIC Value ( Uses var ) =nln +2m 11.4778
SBC Value ( Uses var ) =nln +m*lnn 3133.33
BIC Value ( Uses var ) =see Wei p153 11.5150
R Square = .937337
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98295
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.385 .589E-01 .0000 -6.54
2 2 -.266 .589E-01 .0000 -4.51
3Autoregressive-Factor # 2 12 -.397 .564E-01 .0000 -7.03
ITERATIVELY REVISING MODEL
PLOT OF THE MODEL RESIDUALS BEFORE INTERVENTION DETECTION
DATE ++++ PRIOR TO INTERVENTION DETECTION
1990/ 1 A .00000
1990/ 2 A .00000
1990/ 3 A .00000
1990/ 4 A .00000
1990/ 5 A .00000
1990/ 6 A .00000
1990/ 7 A .00000
1990/ 8 A .00000
1990/ 9 A .00000
1990/ 10 A .00000
1990/ 11 A .00000
1990/ 12 A .00000
1991/ 1 A .00000
1991/ 2 A .00000
1991/ 3 A .00000
1991/ 4 A .00000
1991/ 5 A .00000
1991/ 6 A .00000
1991/ 7 A .00000
1991/ 8 A .00000
1991/ 9 A .00000
1991/ 10 A .00000
1991/ 11 A .00000
1991/ 12 A .00000
1992/ 1 A .00000
1992/ 2 A .00000
1992/ 3 A .00000
1992/ 4 A 133.16
1992/ 5 A -199.36
1992/ 6 A -39.784
1992/ 7 A -18.049
1992/ 8 A -164.48
1992/ 9 A 145.14
1992/ 10 A 211.03
1992/ 11 A 31.813
1992/ 12 A 117.15
1993/ 1 A -286.40
1993/ 2 A -149.51
1993/ 3 A -26.238
1993/ 4 A -51.188
1993/ 5 A 71.592
1993/ 6 A 222.68
1993/ 7 A 146.42
1993/ 8 A 48.874
1993/ 9 A -42.796
1993/ 10 A -133.03
1993/ 11 A 56.716
1993/ 12 A 13.678
1994/ 1 A -157.62
1994/ 2 A -64.460
1994/ 3 A 291.07
1994/ 4 A 117.82
1994/ 5 A 60.684
1994/ 6 A -288.08
1994/ 7 A -67.492
1994/ 8 A 51.981
1994/ 9 A -148.21
1994/ 10 A -166.74
1994/ 11 A -176.94
1994/ 12 A -201.40
1995/ 1 A 30.947
1995/ 2 A 70.776
1995/ 3 A -233.93
1995/ 4 A -485.26
1995/ 5 A 842.37
1995/ 6 A 484.40
1995/ 7 A 264.72
1995/ 8 A 309.41
1995/ 9 A -253.47
1995/ 10 A 58.391
1995/ 11 A -154.94
1995/ 12 A 40.731
1996/ 1 A -.33192
1996/ 2 A 258.19
1996/ 3 A 11.117
1996/ 4 A 539.40
1996/ 5 A -319.67
1996/ 6 A -397.88
1996/ 7 A 56.463
1996/ 8 A -291.83
1996/ 9 A -39.970
1996/ 10 A -103.34
1996/ 11 A 55.456
1996/ 12 A 67.062
1997/ 1 A -28.582
1997/ 2 A 91.372
1997/ 3 A 61.552
1997/ 4 A 151.35
1997/ 5 A -138.94
1997/ 6 A -7.8128
1997/ 7 A 216.85
1997/ 8 A -237.35
1997/ 9 A 212.17
1997/ 10 A 149.17
1997/ 11 A -292.31
1997/ 12 A 244.04
1998/ 1 A -191.19
1998/ 2 A 40.749
1998/ 3 A 299.94
1998/ 4 A 62.770
1998/ 5 A -24.285
1998/ 6 A 441.29
1998/ 7 A -160.99
1998/ 8 A -210.88
1998/ 9 A -475.06
1998/ 10 A -29.731
1998/ 11 A 38.784
1998/ 12 A 87.446
1999/ 1 A -310.63
1999/ 2 A 77.878
1999/ 3 A 261.60
1999/ 4 A 10.699
1999/ 5 A -30.975
1999/ 6 A 202.21
1999/ 7 A -334.19
1999/ 8 A 109.33
1999/ 9 A 85.286
1999/ 10 A -436.02
1999/ 11 A 249.95
1999/ 12 A -169.10
2000/ 1 A -153.94
2000/ 2 A 256.17
2000/ 3 A 150.26
2000/ 4 A -181.47
2000/ 5 A 157.96
2000/ 6 A -296.69
2000/ 7 A -286.24
2000/ 8 A 205.60
2000/ 9 A -68.053
2000/ 10 A 198.90
2000/ 11 A -34.956
2000/ 12 A -470.86
2001/ 1 A 270.77
2001/ 2 A -72.903
2001/ 3 A 130.45
2001/ 4 A -174.50
2001/ 5 A 141.62
2001/ 6 A 128.21
2001/ 7 A 180.59
2001/ 8 A -67.800
2001/ 9 A -462.28
2001/ 10 A -99.966
2001/ 11 A -32.108
2001/ 12 A 299.74
2002/ 1 A 238.99
2002/ 2 A -96.190
2002/ 3 A -516.34
2002/ 4 A 231.91
2002/ 5 A 18.350
2002/ 6 A -572.94
2002/ 7 A 239.42
2002/ 8 A -110.26
2002/ 9 A 337.55
2002/ 10 A 101.23
2002/ 11 A -52.910
2002/ 12 A 324.53
2003/ 1 A -229.19
2003/ 2 A -477.49
2003/ 3 A 236.28
2003/ 4 A -63.203
2003/ 5 A 44.802
2003/ 6 A 179.54
2003/ 7 A 361.93
2003/ 8 A 248.55
2003/ 9 A 191.19
2003/ 10 A -10.082
2003/ 11 A -727.71
2003/ 12 A 190.41
2004/ 1 A -236.66
2004/ 2 A 187.66
2004/ 3 A 444.56
2004/ 4 A 419.28
2004/ 5 A -271.18
2004/ 6 A 290.61
2004/ 7 A -275.20
2004/ 8 A -66.646
2004/ 9 A -173.02
2004/ 10 A -205.18
2004/ 11 A 382.27
2004/ 12 A 66.257
2005/ 1 A -409.91
2005/ 2 A 184.21
2005/ 3 A 248.78
2005/ 4 A 69.969
2005/ 5 A 137.58
2005/ 6 A 435.33
2005/ 7 A -439.44
2005/ 8 A 204.32
2005/ 9 A -197.71
2005/ 10 A -152.79
2005/ 11 A 80.103
2005/ 12 A -450.96
2006/ 1 A 127.74
2006/ 2 A 156.00
2006/ 3 A 188.36
2006/ 4 A -498.46
2006/ 5 A 576.28
2006/ 6 A 257.22
2006/ 7 A -491.70
2006/ 8 A 72.880
2006/ 9 A -498.41
2006/ 10 A 242.09
2006/ 11 A -193.66
2006/ 12 A -101.97
2007/ 1 A 33.484
2007/ 2 A -182.19
2007/ 3 A -143.74
2007/ 4 A -199.26
2007/ 5 A -44.455
2007/ 6 A -622.57
2007/ 7 A 614.05
2007/ 8 A -214.13
2007/ 9 A -749.55
2007/ 10 A 74.437
2007/ 11 A 80.340
2007/ 12 A -281.86
2008/ 1 A 353.37
2008/ 2 A 16.003
2008/ 3 A -777.79
2008/ 4 A 416.60
2008/ 5 A -439.14
2008/ 6 A -355.03
2008/ 7 A 185.53
2008/ 8 A -241.94
2008/ 9 A 1081.7
2008/ 10 A -442.15
2008/ 11 A -576.00
2008/ 12 A 200.64
2009/ 1 A -57.574
2009/ 2 A 97.912
2009/ 3 A -355.78
2009/ 4 A -34.221
2009/ 5 A -218.95
2009/ 6 A 495.34
2009/ 7 A 406.81
2009/ 8 A -278.55
2009/ 9 A 874.83
2009/ 10 A 578.27
2009/ 11 A 672.50
2009/ 12 A -342.41
2010/ 1 A -521.67
2010/ 2 A -371.77
2010/ 3 A 532.34
2010/ 4 A 799.61
2010/ 5 A 65.740
2010/ 6 A -547.25
2010/ 7 A -1685.1
2010/ 8 A -79.413
2010/ 9 A -124.34
2010/ 10 A 158.63
2010/ 11 A 406.37
2010/ 12 A 601.72
2011/ 1 A 355.07
2011/ 2 A -143.65
2011/ 3 A -30.749
2011/ 4 A -305.35
2011/ 5 A -14.515
2011/ 6 A 313.93
2011/ 7 A 388.13
2011/ 8 A 787.13
2011/ 9 A -354.15
2011/ 10 A -327.09
2011/ 11 A -84.440
2011/ 12 A 96.698
2012/ 1 A 346.74
2012/ 2 A 250.99
2012/ 3 A -86.614
2012/ 4 A -225.31
2012/ 5 A 371.76
2012/ 6 A 160.91
2012/ 7 A 603.98
2012/ 8 A 300.44
2012/ 9 A -270.91
2012/ 10 A 175.40
2012/ 11 A -214.63
2012/ 12 A -312.88
2013/ 1 A 66.182
2013/ 2 A -60.584
2013/ 3 A 488.36
2013/ 4 A 444.27
2013/ 5 A 298.36
2013/ 6 A -366.46
2013/ 7 A 571.15
2013/ 8 A -359.82
2013/ 9 A -242.49
2013/ 10 A -495.84
2013/ 11 A -558.77
2013/ 12 A -36.980
2014/ 1 A -367.89
2014/ 2 A 105.65
2014/ 3 A -341.75
2014/ 4 A -32.936
2014/ 5 A 35.920
2014/ 6 A 468.11
2014/ 7 A 63.439
2014/ 8 A -361.08
2014/ 9 A 223.21
2014/ 10 A 194.67
2014/ 11 A -458.49
++++++++++++++++++++++++++++++++++++++++++++++++++++++++
DIAGNOSTIC CHECK #3: THE TIAO TEST FOR CONSTANCY OF THE MEAN OF THE RESIDUALS
(ASSUMING THAT THE ORIGINAL SERIES FOLLOWS AN ARMAX PROCESS)
The Critical Value used for this test : .06
TYPE OF PATTERN CYCLE TIME DATE REGRESSION P VALUE
INTERVENTION (T) WEIGHT
(OUTLIER)
Additive Pulse NA 247 2010/ 7 -1100.98 .0038
Additive Pulse NA 259 2011/ 7 985.594 .0078
Additive Pulse NA 237 2009/ 9 1008.89 .0051
Additive Pulse NA 65 1995/ 5 667.147 .0584
Since the automatic model fixup option for the Outlier Test is enabled, the
program will add variables to the model for the identified outliers, subject
to the USER_S maximum. The program will then start the iterative process
of transfer function model identification, estimation and diagnostic checking.
The forecasts will include the known future values of the intervention
variables in the computation of the output series forecast values.
MODEL STAGE: 160 5EST 4
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.398 .599E-01 .0000 -6.64
2 2 -.297 .589E-01 .0000 -5.05
3Autoregressive-Factor # 2 12 -.434 .572E-01 .0000 -7.57
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -977. 213. .0000 -4.58
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -465. 213. .0300 -2.18
INPUT SERIES X3 I~P00237 PULSE 2009/ 9
Differencing 1
Differencing 12
6Omega (input) -Factor # 5 0 457. 215. .0342 2.13
INPUT SERIES X4 I~P00065 PULSE 1995/ 5
Differencing 1
Differencing 12
7Omega (input) -Factor # 6 0 214. 211. .3118 1.01
MODEL STAGE: 161 6EST 1
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.407 .600E-01 .0000 -6.78
2 2 -.291 .589E-01 .0000 -4.94
3Autoregressive-Factor # 2 12 -.431 .572E-01 .0000 -7.54
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -946. 214. .0000 -4.42
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -502. 214. .0195 -2.35
INPUT SERIES X3 I~P00237 PULSE 2009/ 9
Differencing 1
Differencing 12
6Omega (input) -Factor # 5 0 413. 215. .0562 1.92
INPUT SERIES X4 I~P00065 PULSE 1995/ 5
Differencing 1
Differencing 12
7Omega (input) -Factor # 6 0 203. 212. .3374 .96
MODEL STAGE: 162 7EST 1
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.407 .600E-01 .0000 -6.78
2 2 -.291 .589E-01 .0000 -4.94
3Autoregressive-Factor # 2 12 -.431 .572E-01 .0000 -7.54
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -946. 214. .0000 -4.42
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -503. 214. .0195 -2.35
INPUT SERIES X3 I~P00237 PULSE 2009/ 9
Differencing 1
Differencing 12
6Omega (input) -Factor # 5 0 412. 215. .0566 1.91
INPUT SERIES X4 I~P00065 PULSE 1995/ 5
Differencing 1
Differencing 12
7Omega (input) -Factor # 6 0 203. 212. .3382 .96
(P9>DELETING I~P00065alteryx NOT-SIGNIFICANT
MODEL STAGE: 251 8EST 2
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00247 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00259 2011/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X3 I~P00237 2009/ 9 PULSE
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 265
Residual Mean =Sum R / n .234376
Error/Residual Sum of Squares =Sum R**2 .258899E+08
Variance =SOS/(n) 86588.5
Adjusted Variance =SOS/(n-m) 97697.9
Standard Deviation RMSE =SQRT(Adj Var) 312.567
Standard Error of the Mean =Standard Dev/ (n-m) 19.2008
Mean / its Standard Error =Mean/SEM .122066E-01
Mean Absolute Deviation =Sum(ABS(R))/n 241.785
AIC Value ( Uses var ) =nln +2m 11.4157
SBC Value ( Uses var ) =nln +m*lnn 3131.59
BIC Value ( Uses var ) =see Wei p153 11.5024
R Square = .942665
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.97442
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.414 .589E-01 .0000 -7.03
2 2 -.297 .587E-01 .0000 -5.05
3Autoregressive-Factor # 2 12 -.430 .572E-01 .0000 -7.52
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -947. 215. .0000 -4.41
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -503. 215. .0198 -2.34
INPUT SERIES X3 I~P00237 PULSE 2009/ 9
Differencing 1
Differencing 12
6Omega (input) -Factor # 5 0 409. 216. .0592 1.89
MODEL STAGE: 38 9EST 1
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00247 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00259 2011/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X3 I~P00237 2009/ 9 PULSE
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 266
Residual Mean =Sum R / n .236703
Error/Residual Sum of Squares =Sum R**2 .258898E+08
Variance =SOS/(n) 86588.1
Adjusted Variance =SOS/(n-m) 97330.2
Standard Deviation RMSE =SQRT(Adj Var) 311.978
Standard Error of the Mean =Standard Dev/ (n-m) 19.1286
Mean / its Standard Error =Mean/SEM .123743E-01
Mean Absolute Deviation =Sum(ABS(R))/n 241.758
AIC Value ( Uses var ) =nln +2m 11.4091
SBC Value ( Uses var ) =nln +m*lnn 3125.98
BIC Value ( Uses var ) =see Wei p153 11.4833
R Square = .942665
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.97558
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.414 .589E-01 .0000 -7.03
2 2 -.297 .587E-01 .0000 -5.06
3Autoregressive-Factor # 2 12 -.431 .572E-01 .0000 -7.53
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -952. 215. .0000 -4.43
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -501. 215. .0203 -2.33
INPUT SERIES X3 I~P00237 PULSE 2009/ 9
Differencing 1
Differencing 12
6Omega (input) -Factor # 5 0 414. 216. .0562 1.92
MODEL STAGE: 39 10EST 2
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00247 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00259 2011/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X3 I~P00237 2009/ 9 PULSE
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 266
Residual Mean =Sum R / n .237490
Error/Residual Sum of Squares =Sum R**2 .258898E+08
Variance =SOS/(n) 86588.1
Adjusted Variance =SOS/(n-m) 97330.2
Standard Deviation RMSE =SQRT(Adj Var) 311.978
Standard Error of the Mean =Standard Dev/ (n-m) 19.1286
Mean / its Standard Error =Mean/SEM .124154E-01
Mean Absolute Deviation =Sum(ABS(R))/n 241.751
AIC Value ( Uses var ) =nln +2m 11.4091
SBC Value ( Uses var ) =nln +m*lnn 3125.98
BIC Value ( Uses var ) =see Wei p153 11.4833
R Square = .942665
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.97448
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.414 .589E-01 .0000 -7.03
2 2 -.297 .587E-01 .0000 -5.06
3Autoregressive-Factor # 2 12 -.430 .572E-01 .0000 -7.53
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -952. 215. .0000 -4.43
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -501. 215. .0203 -2.33
INPUT SERIES X3 I~P00237 PULSE 2009/ 9
Differencing 1
Differencing 12
6Omega (input) -Factor # 5 0 414. 216. .0564 1.92
LAG ACF STND. T- CHI-SQUARE & PACF STND. T-
VALUE ERROR RATIO PROBABILITY VALUE ERROR RATIO
1 .009 .061 .14 .0 NA .009 .058 .15
2 -.060 .061 -1.00 1.0 NA -.060 .058 -1.05
3 -.052 .061 -.86 1.8 NA -.052 .058 -.89
4 -.218 .061 -3.57 15.0 NA -.222 .058 -3.84
5 .051 .064 .81 15.7 NA .048 .058 .83
6 .015 .064 .23 15.8 NA -.018 .058 -.31
7 -.026 .064 -.40 16.0 .0001 -.043 .058 -.75
8 .011 .064 .18 16.0 .0003 -.034 .058 -.59
9 .191 .064 2.99 26.4 .0000 .221 .058 3.83
10 -.121 .066 -1.83 30.5 .0000 -.148 .058 -2.56
11 -.019 .067 -.28 30.6 .0000 .000 .058 -.01
12 -.073 .067 -1.08 32.2 .0000 -.078 .058 -1.35
13 -.151 .067 -2.24 38.7 .0000 -.075 .058 -1.29
14 .292 .068 4.27 63.3 .0000 .227 .058 3.93
15 .031 .073 .43 63.6 .0000 .007 .058 .12
16 -.108 .073 -1.48 67.0 .0000 -.133 .058 -2.30
17 -.026 .074 -.36 67.2 .0000 -.033 .058 -.58
18 -.038 .074 -.52 67.7 .0000 .044 .058 .76
19 -.081 .074 -1.10 69.6 .0000 -.102 .058 -1.76
20 .157 .074 2.12 76.9 .0000 .130 .058 2.24
21 .071 .075 .94 78.4 .0000 .104 .058 1.79
22 -.136 .075 -1.81 83.9 .0000 -.143 .058 -2.47
23 .206 .076 2.70 96.6 .0000 .110 .058 1.91
24 -.217 .078 -2.77 110.8 .0000 -.169 .058 -2.92
25 -.086 .081 -1.06 113.0 .0000 -.048 .058 -.83
26 .146 .081 1.81 119.4 .0000 .177 .058 3.06
AUTOBOX WILL NOW ANALYZE THE RESIDUALS IN ORDER TO ASSESS THE SUFFICIENCY AND
TO DETERMINE POSSIBLE MODEL IMPROVEMENTS.
DIAGNOSTIC CHECK #2: THE SUFFICIENCY TEST
AUTOCORRELATION FUNCTION (ACF) PARTIAL ACF
VALUE RATIO -1 0 +1 VALUE RATIO -1 0 +1
1 .009 .14 {*} .009 .15 {*}
2 -.060 -1.00 {*} -.060 -1.05 {*}
3 -.052 -.86 {*} -.052 -.89 {*}
4 -.218 -3.57 *{*} -.222 -3.84 *{*}
5 .051 .81 {*} .048 .83 {*}
6 .015 .23 {*} -.018 -.31 {*}
7 -.026 -.40 {*} -.043 -.75 {*}
8 .011 .18 {*} -.034 -.59 {*}
9 .191 2.99 {*}* .221 3.83 {*}*
10 -.121 -1.83 {*} -.148 -2.56 {*}
11 -.019 -.28 {*} .000 -.01 {*}
12 -.073 -1.08 {*} -.078 -1.35 {*}
13 -.151 -2.24 *{*} -.075 -1.29 {*}
14 .292 4.27 {*}** .227 3.93 {*}*
15 .031 .43 {*} .007 .12 {*}
16 -.108 -1.48 {*} -.133 -2.30 {*}
17 -.026 -.36 {*} -.033 -.58 {*}
18 -.038 -.52 {*} .044 .76 {*}
19 -.081 -1.10 {*} -.102 -1.76 {*}
20 .157 2.12 {*}* .130 2.24 {*}
21 .071 .94 {*} .104 1.79 {*}
22 -.136 -1.81 {*} -.143 -2.47 {*}
23 .206 2.70 {*}* .110 1.91 {*}
24 -.217 -2.77 *{*} -.169 -2.92 *{*}
(M3>DELETING I~P00237alteryx
MODEL STAGE: 48 11EST 1
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00247 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00259 2011/ 7 PULSE
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 267
Residual Mean =Sum R / n .114230
Error/Residual Sum of Squares =Sum R**2 .262380E+08
Variance =SOS/(n) 87752.5
Adjusted Variance =SOS/(n-m) 98269.7
Standard Deviation RMSE =SQRT(Adj Var) 313.480
Standard Error of the Mean =Standard Dev/ (n-m) 19.1847
Mean / its Standard Error =Mean/SEM .595426E-02
Mean Absolute Deviation =Sum(ABS(R))/n 243.604
AIC Value ( Uses var ) =nln +2m 11.4157
SBC Value ( Uses var ) =nln +m*lnn 3124.01
BIC Value ( Uses var ) =see Wei p153 11.4776
R Square = .941894
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.97003
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.415 .590E-01 .0000 -7.03
2 2 -.278 .592E-01 .0000 -4.69
3Autoregressive-Factor # 2 12 -.411 .567E-01 .0000 -7.25
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -966. 217. .0000 -4.46
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -491. 217. .0244 -2.26
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.415 .590E-01 .0000 -7.03
2 2 -.278 .592E-01 .0000 -4.69
3Autoregressive-Factor # 2 12 -.411 .567E-01 .0000 -7.25
INPUT SERIES X1 I~P00247 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -966. 217. .0000 -4.46
INPUT SERIES X2 I~P00259 PULSE 2011/ 7
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 -491. 217. .0244 -2.26
(B15>DELETING INTERVENTION SERIES I~P00259alteryx
(B15>DELETING INTERVENTION SERIES I~P00247alteryx
MODEL STAGE: 49 12EST 2
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
Number of Residuals (R) =n 272
Number of Degrees of Freedom =n-m 269
Residual Mean =Sum R / n .113720
Error/Residual Sum of Squares =Sum R**2 .262380E+08
Variance =SOS/(n) 87752.5
Adjusted Variance =SOS/(n-m) 97539.0
Standard Deviation RMSE =SQRT(Adj Var) 312.312
Standard Error of the Mean =Standard Dev/ (n-m) 19.0420
Mean / its Standard Error =Mean/SEM .597207E-02
Mean Absolute Deviation =Sum(ABS(R))/n 243.602
AIC Value ( Uses var ) =nln +2m 11.4023
SBC Value ( Uses var ) =nln +m*lnn 3112.80
BIC Value ( Uses var ) =see Wei p153 11.4395
R Square = .942005
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.96978
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.415 .589E-01 .0000 -7.04
2 2 -.277 .591E-01 .0000 -4.69
3Autoregressive-Factor # 2 12 -.410 .565E-01 .0000 -7.26
TESTING PARAMETERS
DIAGNOSTIC CHECK #4: THE CHOW PARAMETER CONSTANCY TEST
The Critical value used for this test : .01
The minimum group or interval size was: 119
F TEST TO VERIFY CONSTANCY OF PARAMETERS
CANDIDATE BREAKPOINT F VALUE P VALUE
120 1999/ 12 4.55639 .0039929423
132 2000/ 12 7.41461 .0000906435
144 2001/ 12 8.56839 .0000199732
156 2002/ 12 9.32945 .0000074149
168 2003/ 12 7.55716 .0000751465
180 2004/ 12 9.19764 .0000087995*
* INDICATES THE MOST RECENT SIGNIFICANT BREAK POINT: 1% SIGNIFICANCE LEVEL.
IMPLEMENTING THE BREAKPOINT AT TIME PERIOD 180: 2004/ 12
THUS WE WILL DROP (DELETE) THE FIRST 179 OBSOLETE OBSERVATIONS
AND ANALYZE THE MOST RECENT 120 STATISTICALLY HOMOGENOUS OBSERVATIONS
EVALUATING BREAK POINT 180
EVALUATING THE BREAK POINT AT TIME PERIOD 180
ERROR SUM OF SQUARES GLOBAL : 26238000.52024878
ERROR SUM OF SQUARES REGIME 1 : 8544022.04678309
ERROR SUM OF SQUARES REGIME 2 : 14978291.56528311
REDUCTION DUE TO LOCAL : 2715686.90818258
MEAN SQUARE ERROR DUE TO LOCAL: 905228.96939419 WITH 3
MEAN SQUARE UNDER LOCAL : 98419.72222622 WITH 239
BREAKPOINT= 180 F VALUE= 9.20 P VALUE= .0000 WITH 3, 239 DF
******** ADVISORY ********
PROCEEDING WITH TRIMMED DATA SET
******** ADVISORY ********
******** ADVISORY ********
TSAY VARIANCE CONSTANCY TEST DISABLED(A)
******** ADVISORY ********
MODEL STAGE: 51 29EST 1
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 90
Residual Mean =Sum R / n .270047
Error/Residual Sum of Squares =Sum R**2 .168888E+08
Variance =SOS/(n) 140740.
Adjusted Variance =SOS/(n-m) 187653.
Standard Deviation RMSE =SQRT(Adj Var) 433.189
Standard Error of the Mean =Standard Dev/ (n-m) 45.6621
Mean / its Standard Error =Mean/SEM .591403E-02
Mean Absolute Deviation =Sum(ABS(R))/n 339.020
AIC Value ( Uses var ) =nln +2m 11.9047
SBC Value ( Uses var ) =nln +m*lnn 1116.08
BIC Value ( Uses var ) =see Wei p153 11.9744
R Square = .836660
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.99875
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.276 .103 .0081 -2.69
2 2 -.217 .103 .0364 -2.12
3Autoregressive-Factor # 2 12 -.378 .974E-01 .0002 -3.88
ITERATIVELY REVISING MODEL
PLOT OF THE MODEL RESIDUALS BEFORE INTERVENTION DETECTION
DATE ++++ PRIOR TO INTERVENTION DETECTION
2004/ 12 A .00000
2005/ 1 A .00000
2005/ 2 A .00000
2005/ 3 A .00000
2005/ 4 A .00000
2005/ 5 A .00000
2005/ 6 A .00000
2005/ 7 A .00000
2005/ 8 A .00000
2005/ 9 A .00000
2005/ 10 A .00000
2005/ 11 A .00000
2005/ 12 A .00000
2006/ 1 A .00000
2006/ 2 A .00000
2006/ 3 A .00000
2006/ 4 A .00000
2006/ 5 A .00000
2006/ 6 A .00000
2006/ 7 A .00000
2006/ 8 A .00000
2006/ 9 A .00000
2006/ 10 A .00000
2006/ 11 A .00000
2006/ 12 A .00000
2007/ 1 A .00000
2007/ 2 A .00000
2007/ 3 A -129.81
2007/ 4 A -169.93
2007/ 5 A -37.972
2007/ 6 A -620.92
2007/ 7 A 686.43
2007/ 8 A -276.89
2007/ 9 A -740.85
2007/ 10 A 177.12
2007/ 11 A 69.476
2007/ 12 A -319.36
2008/ 1 A 399.01
2008/ 2 A -11.841
2008/ 3 A -792.82
2008/ 4 A 516.21
2008/ 5 A -475.78
2008/ 6 A -325.85
2008/ 7 A 233.97
2008/ 8 A -268.80
2008/ 9 A 1105.3
2008/ 10 A -545.77
2008/ 11 A -546.60
2008/ 12 A 306.54
2009/ 1 A -106.22
2009/ 2 A 82.204
2009/ 3 A -333.53
2009/ 4 A -9.4383
2009/ 5 A -206.15
2009/ 6 A 511.16
2009/ 7 A 358.11
2009/ 8 A -328.32
2009/ 9 A 894.01
2009/ 10 A 504.22
2009/ 11 A 599.15
2009/ 12 A -396.99
2010/ 1 A -471.62
2010/ 2 A -304.31
2010/ 3 A 560.98
2010/ 4 A 734.71
2010/ 5 A -26.968
2010/ 6 A -549.14
2010/ 7 A -1614.4
2010/ 8 A 103.60
2010/ 9 A -130.63
2010/ 10 A 114.61
2010/ 11 A 390.83
2010/ 12 A 568.45
2011/ 1 A 293.69
2011/ 2 A -167.37
2011/ 3 A -13.708
2011/ 4 A -313.33
2011/ 5 A 10.001
2011/ 6 A 333.29
2011/ 7 A 379.66
2011/ 8 A 740.50
2011/ 9 A -425.88
2011/ 10 A -282.69
2011/ 11 A -28.561
2011/ 12 A 73.840
2012/ 1 A 319.89
2012/ 2 A 218.64
2012/ 3 A -108.63
2012/ 4 A -197.26
2012/ 5 A 401.07
2012/ 6 A 103.88
2012/ 7 A 558.10
2012/ 8 A 236.24
2012/ 9 A -300.12
2012/ 10 A 221.89
2012/ 11 A -226.36
2012/ 12 A -300.08
2013/ 1 A 107.85
2013/ 2 A -76.550
2013/ 3 A 484.69
2013/ 4 A 397.88
2013/ 5 A 237.35
2013/ 6 A -385.36
2013/ 7 A 615.96
2013/ 8 A -416.78
2013/ 9 A -218.30
2013/ 10 A -450.55
2013/ 11 A -514.27
2013/ 12 A 22.889
2014/ 1 A -369.92
2014/ 2 A 136.30
2014/ 3 A -355.06
2014/ 4 A -13.895
2014/ 5 A 45.355
2014/ 6 A 460.91
2014/ 7 A 2.4151
2014/ 8 A -359.86
2014/ 9 A 278.31
2014/ 10 A 180.27
2014/ 11 A -486.45
++++++++++++++++++++++++++++++++++++++++++++++++++++++++
DIAGNOSTIC CHECK #3: THE TIAO TEST FOR CONSTANCY OF THE MEAN OF THE RESIDUALS
(ASSUMING THAT THE ORIGINAL SERIES FOLLOWS AN ARMAX PROCESS)
The Critical Value used for this test : .05
TYPE OF PATTERN CYCLE TIME DATE REGRESSION P VALUE
INTERVENTION (T) WEIGHT
(OUTLIER)
Additive Pulse NA 68 2010/ 7 -1177.34 .0256
Since the automatic model fixup option for the Outlier Test is enabled, the
program will add variables to the model for the identified outliers, subject
to the USER_S maximum. The program will then start the iterative process
of transfer function model identification, estimation and diagnostic checking.
The forecasts will include the known future values of the intervention
variables in the computation of the output series forecast values.
MODEL STAGE: 160 30EST 2
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.329 .103 .0019 -3.18
2 2 -.219 .104 .0370 -2.11
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -794. 272. .0042 -2.92
MODEL STAGE: 161 31EST 1
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
MODEL STAGE: 162 32EST 1
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
MODEL STAGE: 53 33EST 2
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
MODEL STAGE: 7 34EST 3
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 89
Residual Mean =Sum R / n .495689
Error/Residual Sum of Squares =Sum R**2 .154963E+08
Variance =SOS/(n) 129135.
Adjusted Variance =SOS/(n-m) 174115.
Standard Deviation RMSE =SQRT(Adj Var) 417.271
Standard Error of the Mean =Standard Dev/ (n-m) 44.2307
Mean / its Standard Error =Mean/SEM .112069E-01
Mean Absolute Deviation =Sum(ABS(R))/n 332.036
AIC Value ( Uses var ) =nln +2m 11.8353
SBC Value ( Uses var ) =nln +m*lnn 1112.61
BIC Value ( Uses var ) =see Wei p153 11.9282
R Square = .850127
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98006
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
MODEL STAGE: 12 35EST 1
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 89
Residual Mean =Sum R / n .495689
Error/Residual Sum of Squares =Sum R**2 .154963E+08
Variance =SOS/(n) 129135.
Adjusted Variance =SOS/(n-m) 174115.
Standard Deviation RMSE =SQRT(Adj Var) 417.271
Standard Error of the Mean =Standard Dev/ (n-m) 44.2307
Mean / its Standard Error =Mean/SEM .112069E-01
Mean Absolute Deviation =Sum(ABS(R))/n 332.036
AIC Value ( Uses var ) =nln +2m 11.8353
SBC Value ( Uses var ) =nln +m*lnn 1112.61
BIC Value ( Uses var ) =see Wei p153 11.9282
R Square = .850127
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98006
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
MODEL STAGE: 17 36EST 2
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 89
Residual Mean =Sum R / n .495689
Error/Residual Sum of Squares =Sum R**2 .154963E+08
Variance =SOS/(n) 129135.
Adjusted Variance =SOS/(n-m) 174115.
Standard Deviation RMSE =SQRT(Adj Var) 417.271
Standard Error of the Mean =Standard Dev/ (n-m) 44.2307
Mean / its Standard Error =Mean/SEM .112069E-01
Mean Absolute Deviation =Sum(ABS(R))/n 332.036
AIC Value ( Uses var ) =nln +2m 11.8353
SBC Value ( Uses var ) =nln +m*lnn 1112.61
BIC Value ( Uses var ) =see Wei p153 11.9282
R Square = .850127
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98006
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
LAG ACF STND. T- CHI-SQUARE & PACF STND. T-
VALUE ERROR RATIO PROBABILITY VALUE ERROR RATIO
1 .003 .104 .03 .0 NA .003 .091 .03
2 -.053 .104 -.51 .3 NA -.053 .091 -.58
3 -.051 .104 -.49 .5 NA -.051 .091 -.56
4 -.240 .104 -2.30 6.2 NA -.244 .091 -2.67
5 .095 .110 .86 7.1 .0075 .093 .091 1.02
6 .021 .111 .19 7.2 .0276 -.011 .091 -.12
7 .016 .111 .15 7.2 .0655 .004 .091 .04
8 -.015 .111 -.13 7.2 .1242 -.068 .091 -.75
9 .161 .111 1.45 10.0 .0762 .227 .091 2.48
10 -.128 .113 -1.13 11.7 .0687 -.167 .091 -1.83
11 -.088 .115 -.77 12.6 .0837 -.059 .091 -.65
12 -.094 .116 -.81 13.5 .0954 -.134 .091 -1.47
13 -.110 .117 -.95 14.9 .0950 -.013 .091 -.14
14 .358 .118 3.04 29.2 .0012 .264 .091 2.89
15 -.029 .129 -.22 29.3 .0020 -.088 .091 -.97
16 -.079 .129 -.61 30.0 .0028 -.108 .091 -1.18
17 -.012 .129 -.10 30.0 .0047 .021 .091 .23
18 -.116 .129 -.89 31.6 .0045 .003 .091 .03
19 .041 .131 .31 31.8 .0068 -.021 .091 -.23
20 .049 .131 .38 32.1 .0097 .016 .091 .17
21 .094 .131 .72 33.2 .0107 .141 .091 1.54
22 -.117 .132 -.89 34.9 .0098 -.156 .091 -1.70
23 .108 .133 .82 36.4 .0095 .014 .091 .16
24 -.198 .134 -1.48 41.4 .0033 -.200 .091 -2.19
25 -.113 .137 -.82 43.0 .0031 .015 .091 .16
26 .073 .138 .53 43.7 .0038 .026 .091 .28
AUTOBOX WILL NOW ANALYZE THE RESIDUALS IN ORDER TO ASSESS THE SUFFICIENCY AND
TO DETERMINE POSSIBLE MODEL IMPROVEMENTS.
DIAGNOSTIC CHECK #2: THE SUFFICIENCY TEST
AUTOCORRELATION FUNCTION (ACF) PARTIAL ACF
VALUE RATIO -1 0 +1 VALUE RATIO -1 0 +1
1 .003 .03 {*} .003 .03 {*}
2 -.053 -.51 {*} -.053 -.58 {*}
3 -.051 -.49 {*} -.051 -.56 {*}
4 -.240 -2.30 *{*} -.244 -2.67 *{*}
5 .095 .86 {*} .093 1.02 {*}
6 .021 .19 {*} -.011 -.12 {*}
7 .016 .15 {*} .004 .04 {*}
8 -.015 -.13 {*} -.068 -.75 {*}
9 .161 1.45 {*}* .227 2.48 {*}*
10 -.128 -1.13 {*} -.167 -1.83 *{*}
11 -.088 -.77 {*} -.059 -.65 {*}
12 -.094 -.81 {*} -.134 -1.47 {*}
13 -.110 -.95 {*} -.013 -.14 {*}
14 .358 3.04 {*}*** .264 2.89 {*}**
15 -.029 -.22 {*} -.088 -.97 {*}
16 -.079 -.61 {*} -.108 -1.18 {*}
17 -.012 -.10 {*} .021 .23 {*}
18 -.116 -.89 {*} .003 .03 {*}
19 .041 .31 {*} -.021 -.23 {*}
20 .049 .38 {*} .016 .17 {*}
21 .094 .72 {*} .141 1.54 {*}
22 -.117 -.89 {*} -.156 -1.70 *{*}
23 .108 .82 {*} .014 .16 {*}
24 -.198 -1.48 *{*} -.200 -2.19 *{*}
MODEL STAGE: 33 37EST 3
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 89
Residual Mean =Sum R / n .495689
Error/Residual Sum of Squares =Sum R**2 .154963E+08
Variance =SOS/(n) 129135.
Adjusted Variance =SOS/(n-m) 174115.
Standard Deviation RMSE =SQRT(Adj Var) 417.271
Standard Error of the Mean =Standard Dev/ (n-m) 44.2307
Mean / its Standard Error =Mean/SEM .112069E-01
Mean Absolute Deviation =Sum(ABS(R))/n 332.036
AIC Value ( Uses var ) =nln +2m 11.8353
SBC Value ( Uses var ) =nln +m*lnn 1112.61
BIC Value ( Uses var ) =see Wei p153 11.9282
R Square = .850127
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.98006
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.328 .103 .0019 -3.18
2 2 -.220 .104 .0361 -2.12
3Autoregressive-Factor # 2 12 -.364 .101 .0004 -3.62
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -791. 272. .0044 -2.91
VARIANCE CONSTANCY TEST DISABLED(B)
WE WILL RE-EXAMINE THE NEED TO AUGMENT THE MODEL WITH PULSE INTERVENTIONS(B).
MODEL STAGE: 451 38EST 1
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00046 2008/ 9 PULSE
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 88
Residual Mean =Sum R / n .528964
Error/Residual Sum of Squares =Sum R**2 .148159E+08
Variance =SOS/(n) 123466.
Adjusted Variance =SOS/(n-m) 168363.
Standard Deviation RMSE =SQRT(Adj Var) 410.321
Standard Error of the Mean =Standard Dev/ (n-m) 43.7403
Mean / its Standard Error =Mean/SEM .120933E-01
Mean Absolute Deviation =Sum(ABS(R))/n 322.946
AIC Value ( Uses var ) =nln +2m 11.8071
SBC Value ( Uses var ) =nln +m*lnn 1112.97
BIC Value ( Uses var ) =see Wei p153 11.9232
R Square = .856707
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.96977
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.352 .104 .0010 -3.38
2 2 -.195 .106 .0677 -1.84
3Autoregressive-Factor # 2 12 -.357 .104 .0008 -3.43
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -821. 269. .0028 -3.06
INPUT SERIES X2 I~P00046 PULSE 2008/ 9
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 560. 272. .0420 2.06
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.352 .104 .0010 -3.38
2 2 -.195 .106 .0677 -1.84
3Autoregressive-Factor # 2 12 -.357 .104 .0008 -3.43
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -821. 269. .0028 -3.06
INPUT SERIES X2 I~P00046 PULSE 2008/ 9
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 560. 272. .0420 2.06
MODEL STAGE: 452 39EST 2
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00046 2008/ 9 PULSE
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 88
Residual Mean =Sum R / n .532062
Error/Residual Sum of Squares =Sum R**2 .148158E+08
Variance =SOS/(n) 123465.
Adjusted Variance =SOS/(n-m) 168362.
Standard Deviation RMSE =SQRT(Adj Var) 410.319
Standard Error of the Mean =Standard Dev/ (n-m) 43.7402
Mean / its Standard Error =Mean/SEM .121642E-01
Mean Absolute Deviation =Sum(ABS(R))/n 322.915
AIC Value ( Uses var ) =nln +2m 11.8070
SBC Value ( Uses var ) =nln +m*lnn 1112.97
BIC Value ( Uses var ) =see Wei p153 11.9232
R Square = .856708
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.96994
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.351 .104 .0010 -3.37
2 2 -.195 .106 .0680 -1.84
3Autoregressive-Factor # 2 12 -.355 .104 .0009 -3.41
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -818. 268. .0029 -3.05
INPUT SERIES X2 I~P00046 PULSE 2008/ 9
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 557. 272. .0431 2.05
SIGNIFICANCE TESTING !!
MODEL STAGE: 63 40EST 3
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00046 2008/ 9 PULSE
Number of Residuals (R) =n 93
Number of Degrees of Freedom =n-m 88
Residual Mean =Sum R / n .532171
Error/Residual Sum of Squares =Sum R**2 .148158E+08
Variance =SOS/(n) 123465.
Adjusted Variance =SOS/(n-m) 168362.
Standard Deviation RMSE =SQRT(Adj Var) 410.319
Standard Error of the Mean =Standard Dev/ (n-m) 43.7402
Mean / its Standard Error =Mean/SEM .121666E-01
Mean Absolute Deviation =Sum(ABS(R))/n 322.914
AIC Value ( Uses var ) =nln +2m 11.8070
SBC Value ( Uses var ) =nln +m*lnn 1112.97
BIC Value ( Uses var ) =see Wei p153 11.9232
R Square = .856708
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 1.97000
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.351 .104 .0010 -3.37
2 2 -.195 .106 .0680 -1.84
3Autoregressive-Factor # 2 12 -.355 .104 .0009 -3.41
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
4Omega (input) -Factor # 3 0 -818. 268. .0029 -3.05
INPUT SERIES X2 I~P00046 PULSE 2008/ 9
Differencing 1
Differencing 12
5Omega (input) -Factor # 4 0 557. 272. .0431 2.05
MODEL STAGE: 65 41EST 4
THE VALUES ARE TOO LARGE TO TEST FOR A POWER TRANSFORM. PLEASE SCALE YOUR DATA.
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00046 2008/ 9 PULSE
Number of Residuals (R) =n 94
Number of Degrees of Freedom =n-m 90
Residual Mean =Sum R / n -1.99784
Error/Residual Sum of Squares =Sum R**2 .154098E+08
Variance =SOS/(n) 128415.
Adjusted Variance =SOS/(n-m) 171220.
Standard Deviation RMSE =SQRT(Adj Var) 413.787
Standard Error of the Mean =Standard Dev/ (n-m) 43.6170
Mean / its Standard Error =Mean/SEM -.458041E-01
Mean Absolute Deviation =Sum(ABS(R))/n 329.604
AIC Value ( Uses var ) =nln +2m 11.8297
SBC Value ( Uses var ) =nln +m*lnn 1123.90
BIC Value ( Uses var ) =see Wei p153 11.9226
R Square = .850966
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 2.08969
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.302 .998E-01 .0031 -3.03
2Autoregressive-Factor # 2 12 -.359 .103 .0007 -3.48
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
3Omega (input) -Factor # 3 0 -831. 264. .0021 -3.15
INPUT SERIES X2 I~P00046 PULSE 2008/ 9
Differencing 1
Differencing 12
4Omega (input) -Factor # 4 0 614. 272. .0261 2.25
MODEL STATISTICS AND EQUATION FOR THE CURRENT EQUATION (DETAILS FOLLOW).
Estimation/Diagnostic Checking for Variable Y alteryx
: NEWLY IDENTIFIED VARIABLE X1 I~P00068 2010/ 7 PULSE
: NEWLY IDENTIFIED VARIABLE X2 I~P00046 2008/ 9 PULSE
Number of Residuals (R) =n 94
Number of Degrees of Freedom =n-m 90
Residual Mean =Sum R / n -1.99784
Error/Residual Sum of Squares =Sum R**2 .154098E+08
Variance =SOS/(n) 128415.
Adjusted Variance =SOS/(n-m) 171220.
Standard Deviation RMSE =SQRT(Adj Var) 413.787
Standard Error of the Mean =Standard Dev/ (n-m) 43.6170
Mean / its Standard Error =Mean/SEM -.458041E-01
Mean Absolute Deviation =Sum(ABS(R))/n 329.604
AIC Value ( Uses var ) =nln +2m 11.8297
SBC Value ( Uses var ) =nln +m*lnn 1123.90
BIC Value ( Uses var ) =see Wei p153 11.8029
R Square = .850966
Durbin-Watson Statistic =[-A(T-1)]**2/A**2 2.08969
D-W STATISTIC SUGGESTS NO SIGNIFICANT AUTOCORRELATION for lag1.
THE DURBIN-WATSON STATISTIC IS VALID ONLY FOR MODELS THAT HAVE A WHITE NOISE
ERROR TERM AND NO LAGS OF THE Y SERIES. OTHERWISE IT IS INVALID.
IN THIS CASE THE TEST IS INVALID.
ITERATIVELY REVISING MODEL
MODEL COMPONENT LAG COEFF STANDARD P T
# (BOP) ERROR VALUE VALUE
Differencing 1
Differencing 12
1Autoregressive-Factor # 1 1 -.302 .998E-01 .0031 -3.03
2Autoregressive-Factor # 2 12 -.359 .103 .0007 -3.48
INPUT SERIES X1 I~P00068 PULSE 2010/ 7
Differencing 1
Differencing 12
3Omega (input) -Factor # 3 0 -831. 264. .0021 -3.15
INPUT SERIES X2 I~P00046 PULSE 2008/ 9
Differencing 1
Differencing 12
4Omega (input) -Factor # 4 0 614. 272. .0261 2.25
MODELLING OUTPUT SERIES:alteryx
[(1-B**1)][(1-B**12)]Y(T) = alteryx
+[X1(T)][(1-B**1)][(1-B**12)][(- 831.26 )]
+[X2(T)][(1-B**1)][(1-B**12)][(+ 613.63 )]
+ [(1+ .302B** 1)(1+ .359B** 12)]**-1 [A(T)]
LAG ACF STND. T- CHI-SQUARE & PACF STND. T-
VALUE ERROR RATIO PROBABILITY VALUE ERROR RATIO
1 -.055 .103 -.54 .3 NA -.055 .091 -.60
2 -.153 .103 -1.48 2.6 NA -.156 .091 -1.71
3 .042 .106 .40 2.8 NA .024 .091 .27
4 -.247 .106 -2.33 8.9 NA -.275 .091 -3.01
5 .037 .112 .33 9.0 .0027 .020 .091 .22
6 .142 .112 1.26 11.1 .0039 .060 .091 .65
7 -.065 .114 -.57 11.5 .0092 -.034 .091 -.37
8 .005 .114 .04 11.5 .0213 -.035 .091 -.38
9 .210 .114 1.84 16.2 .0063 .228 .091 2.49
10 -.179 .118 -1.51 19.6 .0032 -.133 .091 -1.46
11 -.018 .121 -.15 19.7 .0063 .019 .091 .21
12 -.072 .121 -.60 20.3 .0094 -.166 .091 -1.82
13 -.188 .122 -1.54 24.2 .0040 -.089 .091 -.98
14 .331 .125 2.66 36.6 .0001 .233 .091 2.55
15 .015 .134 .11 36.6 .0001 -.057 .091 -.62
16 -.115 .134 -.86 38.1 .0001 -.047 .091 -.51
17 .086 .135 .64 39.0 .0002 .037 .091 .41
18 -.147 .135 -1.09 41.6 .0001 -.087 .091 -.95
19 -.040 .137 -.29 41.8 .0002 .028 .091 .31
20 .092 .137 .67 42.8 .0003 -.054 .091 -.60
21 .039 .138 .28 43.0 .0005 .133 .091 1.46
22 -.112 .138 -.81 44.6 .0005 -.131 .091 -1.44
23 .205 .139 1.47 49.9 .0001 .115 .091 1.25
24 -.208 .142 -1.47 55.5 .0000 -.240 .091 -2.63
25 -.114 .145 -.79 57.2 .0000 -.005 .091 -.05
26 .124 .146 .85 59.3 .0000 -.013 .091 -.15
AUTOCORRELATION FUNCTION (ACF) PARTIAL ACF
VALUE RATIO -1 0 +1 VALUE RATIO -1 0 +1
1 -.055 -.54 {** } -.055 -.60 {** }
2 -.153 -1.48 {** } -.156 -1.71 {** }
3 .042 .40 { * } .024 .27 { * }
4 -.247 -2.33 {** } -.275 -3.01 *{** }
5 .037 .33 { * } .020 .22 { * }
6 .142 1.26 { **} .060 .65 { **}
7 -.065 -.57 {** } -.034 -.37 { * }
8 .005 .04 { * } -.035 -.38 { * }
9 .210 1.84 { **} .228 2.49 { **}
10 -.179 -1.51 {** } -.133 -1.46 {** }
11 -.018 -.15 { * } .019 .21 { * }
12 -.072 -.60 {** } -.166 -1.82 {** }
13 -.188 -1.54 {** } -.089 -.98 {** }
14 .331 2.66 { **}* .233 2.55 { **}
15 .015 .11 { * } -.057 -.62 {** }
16 -.115 -.86 { ** } -.047 -.51 { * }
17 .086 .64 { ** } .037 .41 { * }
18 -.147 -1.09 { ** } -.087 -.95 {** }
19 -.040 -.29 { * } .028 .31 { * }
20 .092 .67 { ** } -.054 -.60 {** }
21 .039 .28 { * } .133 1.46 { **}
22 -.112 -.81 { ** } -.131 -1.44 {** }
23 .205 1.47 { ***} .115 1.25 { **}
24 -.208 -1.47 {*** } -.240 -2.63 {** }
A HISTOGRAM OF THE MODEL RESIDUALS
LOWER UPPER CUM-PCT #
-929.7 -736.4 .02 2. IIIII
-736.4 -543.2 .09 6. IIIIIIIIIIIIIIII
-543.2 -349.9 .20 11. IIIIIIIIIIIIIIIIIIIIIIIIIIIIII
-349.9 -156.7 .38 17. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
-156.7 36.60 .55 16. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
36.60 229.9 .70 14. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
229.9 423.1 .84 13. IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII
423.1 616.4 .95 10. IIIIIIIIIIIIIIIIIIIIIIIIIII
616.4 809.6 .98 3. IIIIIIII
809.6 1003. .98 0.
1003. 1196. 1.00 2. IIIII
THE AVERAGE IS = -2.00
THE MEDIAN IS = -23.6
THE MINIMUM IS = -930.
THE MAXIMUM IS = .120E+04
THE # OF VALUES= 94
PLOT OF THE FINAL MODEL RESIDUALS
DATE ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2004/ 12 A .00000
2005/ 1 A .00000
2005/ 2 A .00000
2005/ 3 A .00000
2005/ 4 A .00000
2005/ 5 A .00000
2005/ 6 A .00000
2005/ 7 A .00000
2005/ 8 A .00000
2005/ 9 A .00000
2005/ 10 A .00000
2005/ 11 A .00000
2005/ 12 A .00000
2006/ 1 A .00000
2006/ 2 A .00000
2006/ 3 A .00000
2006/ 4 A .00000
2006/ 5 A .00000
2006/ 6 A .00000
2006/ 7 A .00000
2006/ 8 A .00000
2006/ 9 A .00000
2006/ 10 A .00000
2006/ 11 A .00000
2006/ 12 A .00000
2007/ 1 A .00000
2007/ 2 A -171.75
2007/ 3 A -161.56
2007/ 4 A -119.44
2007/ 5 A -28.515
2007/ 6 A -602.52
2007/ 7 A 678.57
2007/ 8 A -123.97
2007/ 9 A -929.71
2007/ 10 A 233.50
2007/ 11 A 261.52
2007/ 12 A -421.93
2008/ 1 A 362.98
2008/ 2 A 105.01
2008/ 3 A -899.80
2008/ 4 A 503.05
2008/ 5 A -258.70
2008/ 6 A -494.29
2008/ 7 A 316.08
2008/ 8 A -177.32
2008/ 9 A 403.01
2008/ 10 A -23.470
2008/ 11 A -625.01
2008/ 12 A 468.95
2009/ 1 A 27.145
2009/ 2 A -63.103
2009/ 3 A -284.06
2009/ 4 A -23.615
2009/ 5 A -136.48
2009/ 6 A 495.39
2009/ 7 A 407.55
2009/ 8 A -436.76
2009/ 9 A 1196.2
2009/ 10 A 376.48
2009/ 11 A 292.89
2009/ 12 A -475.78
2010/ 1 A -547.23
2010/ 2 A -191.91
2010/ 3 A 641.67
2010/ 4 A 772.98
2010/ 5 A -155.43
2010/ 6 A -692.11
2010/ 7 A -734.58
2010/ 8 A -379.05
2010/ 9 A 149.02
2010/ 10 A -187.60
2010/ 11 A 309.74
2010/ 12 A 595.84
2011/ 1 A 229.30
2011/ 2 A -263.27
2011/ 3 A -50.475
2011/ 4 A -264.70
2011/ 5 A -5.1434
2011/ 6 A 409.29
2011/ 7 A -126.96
2011/ 8 A 1032.1
2011/ 9 A -307.57
2011/ 10 A -421.13
2011/ 11 A 103.42
2011/ 12 A 113.22
2012/ 1 A 276.04
2012/ 2 A 212.92
2012/ 3 A -156.70
2012/ 4 A -220.26
2012/ 5 A 439.57
2012/ 6 A 140.07
2012/ 7 A 133.64
2012/ 8 A 445.42
2012/ 9 A -295.57
2012/ 10 A 192.14
2012/ 11 A -126.16
2012/ 12 A -370.97
2013/ 1 A 145.30
2013/ 2 A -8.7022
2013/ 3 A 431.44
2013/ 4 A 426.59
2013/ 5 A 138.53
2013/ 6 A -444.37
2013/ 7 A 589.74
2013/ 8 A -297.49
2013/ 9 A -386.87
2013/ 10 A -349.81
2013/ 11 A -467.74
2013/ 12 A 77.747
2014/ 1 A -282.52
2014/ 2 A 88.395
2014/ 3 A -285.10
2014/ 4 A -71.612
2014/ 5 A 119.08
2014/ 6 A 463.07
2014/ 7 A -20.393
2014/ 8 A -449.59
2014/ 9 A 303.18
2014/ 10 A 289.77
2014/ 11 A -566.51
++++++++++++++++++++++++++++++++++++++++++++++++++++++++
WE ARE FINISHED MODELLING AND NOW REPORT THE FITTED VALUES AND RESIDUALS
TIME DATE H-WEIGHTS TO ACTUAL RESIDUAL %
(T) STABILIZE OBSERVATION FORECAST(FIT) ERROR
180 2004/ 12 1.00 4489.000 NA NA NA
181 2005/ 1 1.00 2981.000 NA NA NA
182 2005/ 2 1.00 3762.000 NA NA NA
183 2005/ 3 1.00 5349.000 NA NA NA
184 2005/ 4 1.00 5261.000 NA NA NA
185 2005/ 5 1.00 5681.000 NA NA NA
186 2005/ 6 1.00 6416.000 NA NA NA
187 2005/ 7 1.00 5808.000 NA NA NA
188 2005/ 8 1.00 6006.000 NA NA NA
189 2005/ 9 1.00 5086.000 NA NA NA
190 2005/ 10 1.00 4696.000 NA NA NA
191 2005/ 11 1.00 4413.000 NA NA NA
192 2005/ 12 1.00 4521.000 NA NA NA
193 2006/ 1 1.00 3457.000 NA NA NA
194 2006/ 2 1.00 4311.000 NA NA NA
195 2006/ 3 1.00 5883.000 NA NA NA
196 2006/ 4 1.00 5332.000 NA NA NA
197 2006/ 5 1.00 6410.000 NA NA NA
198 2006/ 6 1.00 7117.000 NA NA NA
199 2006/ 7 1.00 5965.000 NA NA NA
200 2006/ 8 1.00 6344.000 NA NA NA
201 2006/ 9 1.00 5052.000 NA NA NA
202 2006/ 10 1.00 5033.000 NA NA NA
203 2006/ 11 1.00 4585.000 NA NA NA
204 2006/ 12 1.00 4737.000 NA NA NA
205 2007/ 1 1.00 3626.000 NA NA NA
206 2007/ 2 1.00 4248.000 4419.748 -171.748 -4.04
207 2007/ 3 1.00 5726.000 5887.558 -161.558 -2.82
208 2007/ 4 1.00 5252.000 5371.441 -119.441 -2.27
209 2007/ 5 1.00 6092.000 6120.515 -28.515 -.47
210 2007/ 6 1.00 6207.000 6809.517 -602.517 -9.71
211 2007/ 7 1.00 6111.000 5432.426 678.574 11.10
212 2007/ 8 1.00 6041.000 6164.970 -123.970 -2.05
213 2007/ 9 1.00 4069.000 4998.706 -929.706 -22.85
214 2007/ 10 1.00 4396.000 4162.499 233.501 5.31
215 2007/ 11 1.00 4124.000 3862.480 261.520 6.34
216 2007/ 12 1.00 3803.000 4224.928 -421.928 -11.09
217 2008/ 1 1.00 3210.000 2847.022 362.978 11.31
218 2008/ 2 1.00 3869.000 3763.986 105.014 2.71
219 2008/ 3 1.00 4495.000 5394.800 -899.800 -20.02
220 2008/ 4 1.00 4764.000 4260.947 503.053 10.56
221 2008/ 5 1.00 5198.000 5456.698 -258.698 -4.98
222 2008/ 6 1.00 5180.000 5674.287 -494.287 -9.54
223 2008/ 7 1.00 5125.000 4808.921 316.079 6.17
224 2008/ 8 1.00 4912.000 5089.320 -177.320 -3.61
225 2008/ 9 1.00 4293.000 3889.994 403.006 9.39
226 2008/ 10 1.00 3709.000 3732.470 -23.470 -.63
227 2008/ 11 1.00 2801.000 3426.015 -625.015 -22.31
228 2008/ 12 1.00 3292.000 2823.048 468.952 14.25
229 2009/ 1 1.00 2346.000 2318.855 27.145 1.16
230 2009/ 2 1.00 2979.000 3042.103 -63.103 -2.12
231 2009/ 3 1.00 3631.000 3915.064 -284.064 -7.82
232 2009/ 4 1.00 3694.000 3717.615 -23.615 -.64
233 2009/ 5 1.00 4119.000 4255.480 -136.480 -3.31
234 2009/ 6 1.00 4691.000 4195.610 495.390 10.56
235 2009/ 7 1.00 4865.000 4457.450 407.550 8.38
236 2009/ 8 1.00 4193.000 4629.757 -436.757 -10.42
237 2009/ 9 1.00 4045.000 2848.834 1196.166 29.57
238 2009/ 10 1.00 4150.000 3773.521 376.479 9.07
239 2009/ 11 1.00 3773.000 3480.111 292.889 7.76
240 2009/ 12 1.00 3405.000 3880.784 -475.784 -13.97
241 2010/ 1 1.00 2210.000 2757.228 -547.228 -24.76
242 2010/ 2 1.00 2774.000 2965.908 -191.908 -6.92
243 2010/ 3 1.00 4082.000 3440.325 641.675 15.72
244 2010/ 4 1.00 4791.000 4018.020 772.980 16.13
245 2010/ 5 1.00 4891.000 5046.429 -155.429 -3.18
246 2010/ 6 1.00 4658.000 5350.111 -692.111 -14.86
247 2010/ 7 1.00 3363.000 4097.577 -734.577 -21.84
248 2010/ 8 1.00 3476.000 3855.047 -379.047 -10.90
249 2010/ 9 1.00 3151.000 3001.983 149.017 4.73
250 2010/ 10 1.00 2977.000 3164.604 -187.604 -6.30
251 2010/ 11 1.00 2795.000 2485.257 309.743 11.08
252 2010/ 12 1.00 3215.000 2619.165 595.835 18.53
253 2011/ 1 1.00 2194.000 1964.698 229.302 10.45
254 2011/ 2 1.00 2494.000 2757.269 -263.269 -10.56
255 2011/ 3 1.00 3603.000 3653.475 -50.475 -1.40
256 2011/ 4 1.00 3804.000 4068.702 -264.702 -6.96
257 2011/ 5 1.00 4099.000 4104.143 -5.143 -.13
258 2011/ 6 1.00 4541.000 4131.711 409.289 9.01
259 2011/ 7 1.00 4063.000 4189.956 -126.956 -3.12
260 2011/ 8 1.00 4467.000 3434.928 1032.072 23.10
261 2011/ 9 1.00 3564.000 3871.573 -307.573 -8.63
262 2011/ 10 1.00 3263.000 3684.130 -421.130 -12.91
263 2011/ 11 1.00 3183.000 3079.580 103.420 3.25
264 2011/ 12 1.00 3381.000 3267.784 113.216 3.35
265 2012/ 1 1.00 2555.000 2278.963 276.037 10.80
266 2012/ 2 1.00 3085.000 2872.077 212.923 6.90
267 2012/ 3 1.00 4068.000 4224.703 -156.703 -3.85
268 2012/ 4 1.00 4291.000 4511.264 -220.264 -5.13
269 2012/ 5 1.00 5004.000 4564.431 439.569 8.78
270 2012/ 6 1.00 5196.000 5055.925 140.075 2.70
271 2012/ 7 1.00 4859.000 4725.359 133.641 2.75
272 2012/ 8 1.00 5264.000 4818.585 445.415 8.46
273 2012/ 9 1.00 4151.000 4446.575 -295.575 -7.12
274 2012/ 10 1.00 4214.000 4021.860 192.140 4.56
275 2012/ 11 1.00 3875.000 4001.155 -126.155 -3.26
276 2012/ 12 1.00 3849.000 4219.967 -370.967 -9.64
277 2013/ 1 1.00 3190.000 3044.697 145.303 4.55
278 2013/ 2 1.00 3557.000 3565.702 -8.702 -.24
279 2013/ 3 1.00 5041.000 4609.558 431.442 8.56
280 2013/ 4 1.00 5545.000 5118.408 426.592 7.69
281 2013/ 5 1.00 6159.000 6020.475 138.525 2.25
282 2013/ 6 1.00 5981.000 6425.375 -444.375 -7.43
283 2013/ 7 1.00 6322.000 5732.258 589.742 9.33
284 2013/ 8 1.00 6209.000 6506.485 -297.485 -4.79
285 2013/ 9 1.00 4941.000 5327.873 -386.873 -7.83
286 2013/ 10 1.00 4593.000 4942.805 -349.805 -7.62
287 2013/ 11 1.00 3964.000 4431.738 -467.738 -11.80
288 2013/ 12 1.00 4212.000 4134.253 77.747 1.85
289 2014/ 1 1.00 3152.000 3434.518 -282.518 -8.96
290 2014/ 2 1.00 3769.000 3680.605 88.395 2.35
291 2014/ 3 1.00 4730.000 5015.098 -285.098 -6.03
292 2014/ 4 1.00 5165.000 5236.612 -71.612 -1.39
293 2014/ 5 1.00 5924.000 5804.915 119.085 2.01
294 2014/ 6 1.00 6309.000 5845.927 463.073 7.34
295 2014/ 7 1.00 6256.000 6276.393 -20.393 -.33
296 2014/ 8 1.00 5925.000 6374.594 -449.594 -7.59
297 2014/ 9 1.00 5138.000 4834.825 303.175 5.90
298 2014/ 10 1.00 5099.000 4809.229 289.771 5.68
299 2014/ 11 1.00 3959.000 4525.505 -566.505 -14.31
BASED UPON 120 ESTIMATES
MODEL MEAN ABS PCT ERROR = 7.680451
RANDOM WALK MEAN ABS PCT ERROR = 16.513716
AVERAGE MEAN ABS PCT ERROR = 26.793799
TIME DATE LOWER80 % UPPER 80% FORECAST ACTUAL RESIDUAL %
(T) LIMIT LIMIT (IF KNOWN) ERROR
300 2014/ 12 3765.00 4824.00 4294.00 4737.00 .44E+03 9.35
301 2015/ 1 2676.00 3968.00 3322.00 3069.00 -.25E+03 -8.24
302 2015/ 2 3097.00 4636.00 3866.00 3780.00 -86. -2.28
303 2015/ 3 4142.00 5879.00 5010.00 5023.00 13. .26
304 2015/ 4 4512.00 6431.00 5472.00 5375.00 -97. -1.80
305 2015/ 5 5136.00 7220.00 6178.00 5983.00 -.20E+03 -3.26
306 2015/ 6 5242.00 7480.00 6361.00 6612.00 .25E+03 3.80
307 2015/ 7 5259.00 7640.00 6449.00 6731.00 .28E+03 4.19
308 2015/ 8 4939.00 7455.00 6197.00 6344.00 .15E+03 2.32
309 2015/ 9 3915.00 6559.00 5237.00 5599.00 .36E+03 6.47
310 2015/ 10 3704.00 6470.00 5087.00 5015.00 -72. -1.44
311 2015/ 11 2689.00 5572.00 4131.00 4252.00 .12E+03 2.85
TOTAL 61604.00 62520.00 .92E+03 1.47
MEAN ABSOLUTE PERCENT ERROR = 3.854
WEIGHTED MEAN ABSOLUTE PERCENT ERROR = 3.714
A = alteryx
F = FORECASTS
DATE ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
2004/ 12 A 4489.0
2005/ 1 A 2981.0
2005/ 2 A 3762.0
2005/ 3 A 5349.0
2005/ 4 A 5261.0
2005/ 5 A 5681.0
2005/ 6 A 6416.0
2005/ 7 A 5808.0
2005/ 8 A 6006.0
2005/ 9 A 5086.0
2005/ 10 A 4696.0
2005/ 11 A 4413.0
2005/ 12 A 4521.0
2006/ 1 A 3457.0
2006/ 2 A 4311.0
2006/ 3 A 5883.0
2006/ 4 A 5332.0
2006/ 5 A 6410.0
2006/ 6 A 7117.0
2006/ 7 A 5965.0
2006/ 8 A 6344.0
2006/ 9 A 5052.0
2006/ 10 A 5033.0
2006/ 11 A 4585.0
2006/ 12 A 4737.0
2007/ 1 A 3626.0
2007/ 2 A 4248.0
2007/ 3 A 5726.0
2007/ 4 A 5252.0
2007/ 5 A 6092.0
2007/ 6 A 6207.0
2007/ 7 A 6111.0
2007/ 8 A 6041.0
2007/ 9 A 4069.0
2007/ 10 A 4396.0
2007/ 11 A 4124.0
2007/ 12 A 3803.0
2008/ 1 A 3210.0
2008/ 2 A 3869.0
2008/ 3 A 4495.0
2008/ 4 A 4764.0
2008/ 5 A 5198.0
2008/ 6 A 5180.0
2008/ 7 A 5125.0
2008/ 8 A 4912.0
2008/ 9 A 4293.0
2008/ 10 A 3709.0
2008/ 11 A 2801.0
2008/ 12 A 3292.0
2009/ 1 A 2346.0
2009/ 2 A 2979.0
2009/ 3 A 3631.0
2009/ 4 A 3694.0
2009/ 5 A 4119.0
2009/ 6 A 4691.0
2009/ 7 A 4865.0
2009/ 8 A 4193.0
2009/ 9 A 4045.0
2009/ 10 A 4150.0
2009/ 11 A 3773.0
2009/ 12 A 3405.0
2010/ 1 A 2210.0
2010/ 2 A 2774.0
2010/ 3 A 4082.0
2010/ 4 A 4791.0
2010/ 5 A 4891.0
2010/ 6 A 4658.0
2010/ 7 A 3363.0
2010/ 8 A 3476.0
2010/ 9 A 3151.0
2010/ 10 A 2977.0
2010/ 11 A 2795.0
2010/ 12 A 3215.0
2011/ 1 A 2194.0
2011/ 2 A 2494.0
2011/ 3 A 3603.0
2011/ 4 A 3804.0
2011/ 5 A 4099.0
2011/ 6 A 4541.0
2011/ 7 A 4063.0
2011/ 8 A 4467.0
2011/ 9 A 3564.0
2011/ 10 A 3263.0
2011/ 11 A 3183.0
2011/ 12 A 3381.0
2012/ 1 A 2555.0
2012/ 2 A 3085.0
2012/ 3 A 4068.0
2012/ 4 A 4291.0
2012/ 5 A 5004.0
2012/ 6 A 5196.0
2012/ 7 A 4859.0
2012/ 8 A 5264.0
2012/ 9 A 4151.0
2012/ 10 A 4214.0
2012/ 11 A 3875.0
2012/ 12 A 3849.0
2013/ 1 A 3190.0
2013/ 2 A 3557.0
2013/ 3 A 5041.0
2013/ 4 A 5545.0
2013/ 5 A 6159.0
2013/ 6 A 5981.0
2013/ 7 A 6322.0
2013/ 8 A 6209.0
2013/ 9 A 4941.0
2013/ 10 A 4593.0
2013/ 11 A 3964.0
2013/ 12 A 4212.0
2014/ 1 A 3152.0
2014/ 2 A 3769.0
2014/ 3 A 4730.0
2014/ 4 A 5165.0
2014/ 5 A 5924.0
2014/ 6 A 6309.0
2014/ 7 A 6256.0
2014/ 8 A 5925.0
2014/ 9 A 5138.0
2014/ 10 A 5099.0
2014/ 11 A 3959.0
2014/ 12 F 4294.0
2015/ 1 F 3322.0
2015/ 2 F 3866.0
2015/ 3 F 5010.0
2015/ 4 F 5472.0
2015/ 5 F 6178.0
2015/ 6 F 6361.0
2015/ 7 F 6449.0
2015/ 8 F 6197.0
2015/ 9 F 5237.0
2015/ 10 F 5087.0
2015/ 11 F 4131.0
++++++++++++++++++++++++++++++++++++++++++++++++++++++++
THE WORK WAS STARTED AT: Time=15:29:32
IT IS TIME TO STOP AND ADMIRE THE WORK ! Time=15:29:48
*Ni_Houlihan
*BREAK.AFS
*NEW.AFS
*C13.AFS