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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationFri, 04 Dec 2009 07:55:47 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/04/t12599391391u79ira5o966l5s.htm/, Retrieved Sun, 28 Apr 2024 14:23:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=63713, Retrieved Sun, 28 Apr 2024 14:23:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMP   [Standard Deviation-Mean Plot] [Taak 8 stap 1] [2008-12-12 12:09:36] [491a70d26f8c977398d8a0c1c87d3dd4]
-    D    [Standard Deviation-Mean Plot] [paper standard de...] [2008-12-12 14:44:37] [491a70d26f8c977398d8a0c1c87d3dd4]
- RM D      [Variance Reduction Matrix] [paper variance re...] [2008-12-12 14:54:46] [491a70d26f8c977398d8a0c1c87d3dd4]
- RMP         [ARIMA Backward Selection] [Paper ARIMA backw...] [2008-12-16 19:22:07] [491a70d26f8c977398d8a0c1c87d3dd4]
- RM            [ARIMA Forecasting] [Paper Arima forec...] [2008-12-16 19:55:16] [491a70d26f8c977398d8a0c1c87d3dd4]
-  MPD              [ARIMA Forecasting] [Ws 9] [2009-12-04 14:55:47] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-                     [ARIMA Forecasting] [Workshop 9-1] [2009-12-04 21:41:47] [aba88da643e3763d32ff92bd8f92a385]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
62
64
62
64
64
69
69
65
56
58
53
62
55
60
59
58
53
57
57
53
54
53
57
57
55
49
50
49
54
58
58
52
56
52
59
53
52
53
51
50
56
52
46
48
46
48
48
49
53
48
51
48
50
55
52
53
52
55
53
53
56
54
52
55
54
59
56
56
51
53
52
51
46
49
46
55
57
53
52
53
50
54
53
50
51
52
47
51
49
53
52
45
53
51
48
48
48
48
40
43
40
39
39
36
41
39
40
39
46
40
37
37
44
41
40
36
38
43
42
45
46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63713&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63713&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63713&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[109])
9748-------
9848-------
9940-------
10043-------
10140-------
10239-------
10339-------
10436-------
10541-------
10639-------
10740-------
10839-------
10946-------
1104042.181636.197148.16610.23750.10550.02840.1055
1113743.362936.657450.06850.03150.83720.83720.2204
1123744.29336.596251.98970.03160.96840.6290.3319
1134445.160936.871753.450.39190.97320.88880.4214
1144143.714634.7952.63910.27550.4750.84980.3079
1154043.966734.390753.54270.20840.72820.84530.3386
1163643.396833.070853.72290.08020.74050.91980.3106
1173843.171732.050254.29320.1810.89690.6490.3091
1184343.496931.602555.39130.46740.81750.77070.34
1194244.652932.085957.21990.33950.60170.7660.4168
1204544.431.284557.51540.46430.64010.79020.4055
1214647.403333.847460.95920.41960.63590.58040.5804

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast \tabularnewline
time & Y[t] & F[t] & 95% LB & 95% UB & p-value(H0: Y[t] = F[t]) & P(F[t]>Y[t-1]) & P(F[t]>Y[t-s]) & P(F[t]>Y[109]) \tabularnewline
97 & 48 & - & - & - & - & - & - & - \tabularnewline
98 & 48 & - & - & - & - & - & - & - \tabularnewline
99 & 40 & - & - & - & - & - & - & - \tabularnewline
100 & 43 & - & - & - & - & - & - & - \tabularnewline
101 & 40 & - & - & - & - & - & - & - \tabularnewline
102 & 39 & - & - & - & - & - & - & - \tabularnewline
103 & 39 & - & - & - & - & - & - & - \tabularnewline
104 & 36 & - & - & - & - & - & - & - \tabularnewline
105 & 41 & - & - & - & - & - & - & - \tabularnewline
106 & 39 & - & - & - & - & - & - & - \tabularnewline
107 & 40 & - & - & - & - & - & - & - \tabularnewline
108 & 39 & - & - & - & - & - & - & - \tabularnewline
109 & 46 & - & - & - & - & - & - & - \tabularnewline
110 & 40 & 42.1816 & 36.1971 & 48.1661 & 0.2375 & 0.1055 & 0.0284 & 0.1055 \tabularnewline
111 & 37 & 43.3629 & 36.6574 & 50.0685 & 0.0315 & 0.8372 & 0.8372 & 0.2204 \tabularnewline
112 & 37 & 44.293 & 36.5962 & 51.9897 & 0.0316 & 0.9684 & 0.629 & 0.3319 \tabularnewline
113 & 44 & 45.1609 & 36.8717 & 53.45 & 0.3919 & 0.9732 & 0.8888 & 0.4214 \tabularnewline
114 & 41 & 43.7146 & 34.79 & 52.6391 & 0.2755 & 0.475 & 0.8498 & 0.3079 \tabularnewline
115 & 40 & 43.9667 & 34.3907 & 53.5427 & 0.2084 & 0.7282 & 0.8453 & 0.3386 \tabularnewline
116 & 36 & 43.3968 & 33.0708 & 53.7229 & 0.0802 & 0.7405 & 0.9198 & 0.3106 \tabularnewline
117 & 38 & 43.1717 & 32.0502 & 54.2932 & 0.181 & 0.8969 & 0.649 & 0.3091 \tabularnewline
118 & 43 & 43.4969 & 31.6025 & 55.3913 & 0.4674 & 0.8175 & 0.7707 & 0.34 \tabularnewline
119 & 42 & 44.6529 & 32.0859 & 57.2199 & 0.3395 & 0.6017 & 0.766 & 0.4168 \tabularnewline
120 & 45 & 44.4 & 31.2845 & 57.5154 & 0.4643 & 0.6401 & 0.7902 & 0.4055 \tabularnewline
121 & 46 & 47.4033 & 33.8474 & 60.9592 & 0.4196 & 0.6359 & 0.5804 & 0.5804 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63713&T=1

[TABLE]
[ROW][C]Univariate ARIMA Extrapolation Forecast[/C][/ROW]
[ROW][C]time[/C][C]Y[t][/C][C]F[t][/C][C]95% LB[/C][C]95% UB[/C][C]p-value(H0: Y[t] = F[t])[/C][C]P(F[t]>Y[t-1])[/C][C]P(F[t]>Y[t-s])[/C][C]P(F[t]>Y[109])[/C][/ROW]
[ROW][C]97[/C][C]48[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]48[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]99[/C][C]40[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]100[/C][C]43[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]101[/C][C]40[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]102[/C][C]39[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]103[/C][C]39[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]104[/C][C]36[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]105[/C][C]41[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]106[/C][C]39[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]107[/C][C]40[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]108[/C][C]39[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]109[/C][C]46[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]110[/C][C]40[/C][C]42.1816[/C][C]36.1971[/C][C]48.1661[/C][C]0.2375[/C][C]0.1055[/C][C]0.0284[/C][C]0.1055[/C][/ROW]
[ROW][C]111[/C][C]37[/C][C]43.3629[/C][C]36.6574[/C][C]50.0685[/C][C]0.0315[/C][C]0.8372[/C][C]0.8372[/C][C]0.2204[/C][/ROW]
[ROW][C]112[/C][C]37[/C][C]44.293[/C][C]36.5962[/C][C]51.9897[/C][C]0.0316[/C][C]0.9684[/C][C]0.629[/C][C]0.3319[/C][/ROW]
[ROW][C]113[/C][C]44[/C][C]45.1609[/C][C]36.8717[/C][C]53.45[/C][C]0.3919[/C][C]0.9732[/C][C]0.8888[/C][C]0.4214[/C][/ROW]
[ROW][C]114[/C][C]41[/C][C]43.7146[/C][C]34.79[/C][C]52.6391[/C][C]0.2755[/C][C]0.475[/C][C]0.8498[/C][C]0.3079[/C][/ROW]
[ROW][C]115[/C][C]40[/C][C]43.9667[/C][C]34.3907[/C][C]53.5427[/C][C]0.2084[/C][C]0.7282[/C][C]0.8453[/C][C]0.3386[/C][/ROW]
[ROW][C]116[/C][C]36[/C][C]43.3968[/C][C]33.0708[/C][C]53.7229[/C][C]0.0802[/C][C]0.7405[/C][C]0.9198[/C][C]0.3106[/C][/ROW]
[ROW][C]117[/C][C]38[/C][C]43.1717[/C][C]32.0502[/C][C]54.2932[/C][C]0.181[/C][C]0.8969[/C][C]0.649[/C][C]0.3091[/C][/ROW]
[ROW][C]118[/C][C]43[/C][C]43.4969[/C][C]31.6025[/C][C]55.3913[/C][C]0.4674[/C][C]0.8175[/C][C]0.7707[/C][C]0.34[/C][/ROW]
[ROW][C]119[/C][C]42[/C][C]44.6529[/C][C]32.0859[/C][C]57.2199[/C][C]0.3395[/C][C]0.6017[/C][C]0.766[/C][C]0.4168[/C][/ROW]
[ROW][C]120[/C][C]45[/C][C]44.4[/C][C]31.2845[/C][C]57.5154[/C][C]0.4643[/C][C]0.6401[/C][C]0.7902[/C][C]0.4055[/C][/ROW]
[ROW][C]121[/C][C]46[/C][C]47.4033[/C][C]33.8474[/C][C]60.9592[/C][C]0.4196[/C][C]0.6359[/C][C]0.5804[/C][C]0.5804[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63713&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63713&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Univariate ARIMA Extrapolation Forecast
timeY[t]F[t]95% LB95% UBp-value(H0: Y[t] = F[t])P(F[t]>Y[t-1])P(F[t]>Y[t-s])P(F[t]>Y[109])
9748-------
9848-------
9940-------
10043-------
10140-------
10239-------
10339-------
10436-------
10541-------
10639-------
10740-------
10839-------
10946-------
1104042.181636.197148.16610.23750.10550.02840.1055
1113743.362936.657450.06850.03150.83720.83720.2204
1123744.29336.596251.98970.03160.96840.6290.3319
1134445.160936.871753.450.39190.97320.88880.4214
1144143.714634.7952.63910.27550.4750.84980.3079
1154043.966734.390753.54270.20840.72820.84530.3386
1163643.396833.070853.72290.08020.74050.91980.3106
1173843.171732.050254.29320.1810.89690.6490.3091
1184343.496931.602555.39130.46740.81750.77070.34
1194244.652932.085957.21990.33950.60170.7660.4168
1204544.431.284557.51540.46430.64010.79020.4055
1214647.403333.847460.95920.41960.63590.58040.5804






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
1100.0724-0.05170.00434.75940.39660.6298
1110.0789-0.14670.012240.48713.37391.8368
1120.0887-0.16470.013753.18724.43232.1053
1130.0936-0.02570.00211.34760.11230.3351
1140.1042-0.06210.00527.36880.61410.7836
1150.1111-0.09020.007515.7351.31131.1451
1160.1214-0.17040.014254.71314.55942.1353
1170.1314-0.11980.0126.74642.22891.4929
1180.1395-0.01140.0010.24690.02060.1434
1190.1436-0.05940.0057.0380.58650.7658
1200.15070.01350.00110.360.030.1732
1210.1459-0.02960.00251.96910.16410.4051

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
110 & 0.0724 & -0.0517 & 0.0043 & 4.7594 & 0.3966 & 0.6298 \tabularnewline
111 & 0.0789 & -0.1467 & 0.0122 & 40.4871 & 3.3739 & 1.8368 \tabularnewline
112 & 0.0887 & -0.1647 & 0.0137 & 53.1872 & 4.4323 & 2.1053 \tabularnewline
113 & 0.0936 & -0.0257 & 0.0021 & 1.3476 & 0.1123 & 0.3351 \tabularnewline
114 & 0.1042 & -0.0621 & 0.0052 & 7.3688 & 0.6141 & 0.7836 \tabularnewline
115 & 0.1111 & -0.0902 & 0.0075 & 15.735 & 1.3113 & 1.1451 \tabularnewline
116 & 0.1214 & -0.1704 & 0.0142 & 54.7131 & 4.5594 & 2.1353 \tabularnewline
117 & 0.1314 & -0.1198 & 0.01 & 26.7464 & 2.2289 & 1.4929 \tabularnewline
118 & 0.1395 & -0.0114 & 0.001 & 0.2469 & 0.0206 & 0.1434 \tabularnewline
119 & 0.1436 & -0.0594 & 0.005 & 7.038 & 0.5865 & 0.7658 \tabularnewline
120 & 0.1507 & 0.0135 & 0.0011 & 0.36 & 0.03 & 0.1732 \tabularnewline
121 & 0.1459 & -0.0296 & 0.0025 & 1.9691 & 0.1641 & 0.4051 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=63713&T=2

[TABLE]
[ROW][C]Univariate ARIMA Extrapolation Forecast Performance[/C][/ROW]
[ROW][C]time[/C][C]% S.E.[/C][C]PE[/C][C]MAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][/ROW]
[ROW][C]110[/C][C]0.0724[/C][C]-0.0517[/C][C]0.0043[/C][C]4.7594[/C][C]0.3966[/C][C]0.6298[/C][/ROW]
[ROW][C]111[/C][C]0.0789[/C][C]-0.1467[/C][C]0.0122[/C][C]40.4871[/C][C]3.3739[/C][C]1.8368[/C][/ROW]
[ROW][C]112[/C][C]0.0887[/C][C]-0.1647[/C][C]0.0137[/C][C]53.1872[/C][C]4.4323[/C][C]2.1053[/C][/ROW]
[ROW][C]113[/C][C]0.0936[/C][C]-0.0257[/C][C]0.0021[/C][C]1.3476[/C][C]0.1123[/C][C]0.3351[/C][/ROW]
[ROW][C]114[/C][C]0.1042[/C][C]-0.0621[/C][C]0.0052[/C][C]7.3688[/C][C]0.6141[/C][C]0.7836[/C][/ROW]
[ROW][C]115[/C][C]0.1111[/C][C]-0.0902[/C][C]0.0075[/C][C]15.735[/C][C]1.3113[/C][C]1.1451[/C][/ROW]
[ROW][C]116[/C][C]0.1214[/C][C]-0.1704[/C][C]0.0142[/C][C]54.7131[/C][C]4.5594[/C][C]2.1353[/C][/ROW]
[ROW][C]117[/C][C]0.1314[/C][C]-0.1198[/C][C]0.01[/C][C]26.7464[/C][C]2.2289[/C][C]1.4929[/C][/ROW]
[ROW][C]118[/C][C]0.1395[/C][C]-0.0114[/C][C]0.001[/C][C]0.2469[/C][C]0.0206[/C][C]0.1434[/C][/ROW]
[ROW][C]119[/C][C]0.1436[/C][C]-0.0594[/C][C]0.005[/C][C]7.038[/C][C]0.5865[/C][C]0.7658[/C][/ROW]
[ROW][C]120[/C][C]0.1507[/C][C]0.0135[/C][C]0.0011[/C][C]0.36[/C][C]0.03[/C][C]0.1732[/C][/ROW]
[ROW][C]121[/C][C]0.1459[/C][C]-0.0296[/C][C]0.0025[/C][C]1.9691[/C][C]0.1641[/C][C]0.4051[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=63713&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=63713&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
1100.0724-0.05170.00434.75940.39660.6298
1110.0789-0.14670.012240.48713.37391.8368
1120.0887-0.16470.013753.18724.43232.1053
1130.0936-0.02570.00211.34760.11230.3351
1140.1042-0.06210.00527.36880.61410.7836
1150.1111-0.09020.007515.7351.31131.1451
1160.1214-0.17040.014254.71314.55942.1353
1170.1314-0.11980.0126.74642.22891.4929
1180.1395-0.01140.0010.24690.02060.1434
1190.1436-0.05940.0057.0380.58650.7658
1200.15070.01350.00110.360.030.1732
1210.1459-0.02960.00251.96910.16410.4051


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 2 ; par8 = 2 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 2 ; par8 = 2 ; par9 = 1 ; par10 = FALSE ;
R code (references can be found in the software module):
par1 <- as.numeric(par1) #cut off periods
par2 <- as.numeric(par2) #lambda
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #p
par7 <- as.numeric(par7) #q
par8 <- as.numeric(par8) #P
par9 <- as.numeric(par9) #Q
if (par10 == 'TRUE') par10 <- TRUE
if (par10 == 'FALSE') par10 <- FALSE
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
lx <- length(x)
first <- lx - 2*par1
nx <- lx - par1
nx1 <- nx + 1
fx <- lx - nx
if (fx < 1) {
fx <- par5
nx1 <- lx + fx - 1
first <- lx - 2*fx
}
first <- 1
if (fx < 3) fx <- round(lx/10,0)
(arima.out <- arima(x[1:nx], order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5), include.mean=par10, method='ML'))
(forecast <- predict(arima.out,fx))
(lb <- forecast$pred - 1.96 * forecast$se)
(ub <- forecast$pred + 1.96 * forecast$se)
if (par2 == 0) {
x <- exp(x)
forecast$pred <- exp(forecast$pred)
lb <- exp(lb)
ub <- exp(ub)
}
if (par2 != 0) {
x <- x^(1/par2)
forecast$pred <- forecast$pred^(1/par2)
lb <- lb^(1/par2)
ub <- ub^(1/par2)
}
if (par2 < 0) {
olb <- lb
lb <- ub
ub <- olb
}
(actandfor <- c(x[1:nx], forecast$pred))
(perc.se <- (ub-forecast$pred)/1.96/forecast$pred)
bitmap(file='test1.png')
opar <- par(mar=c(4,4,2,2),las=1)
ylim <- c( min(x[first:nx],lb), max(x[first:nx],ub))
plot(x,ylim=ylim,type='n',xlim=c(first,lx))
usr <- par('usr')
rect(usr[1],usr[3],nx+1,usr[4],border=NA,col='lemonchiffon')
rect(nx1,usr[3],usr[2],usr[4],border=NA,col='lavender')
abline(h= (-3:3)*2 , col ='gray', lty =3)
polygon( c(nx1:lx,lx:nx1), c(lb,rev(ub)), col = 'orange', lty=2,border=NA)
lines(nx1:lx, lb , lty=2)
lines(nx1:lx, ub , lty=2)
lines(x, lwd=2)
lines(nx1:lx, forecast$pred , lwd=2 , col ='white')
box()
par(opar)
dev.off()
prob.dec <- array(NA, dim=fx)
prob.sdec <- array(NA, dim=fx)
prob.ldec <- array(NA, dim=fx)
prob.pval <- array(NA, dim=fx)
perf.pe <- array(0, dim=fx)
perf.mape <- array(0, dim=fx)
perf.se <- array(0, dim=fx)
perf.mse <- array(0, dim=fx)
perf.rmse <- array(0, dim=fx)
for (i in 1:fx) {
locSD <- (ub[i] - forecast$pred[i]) / 1.96
perf.pe[i] = (x[nx+i] - forecast$pred[i]) / forecast$pred[i]
perf.mape[i] = perf.mape[i] + abs(perf.pe[i])
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
perf.mse[i] = perf.mse[i] + perf.se[i]
prob.dec[i] = pnorm((x[nx+i-1] - forecast$pred[i]) / locSD)
prob.sdec[i] = pnorm((x[nx+i-par5] - forecast$pred[i]) / locSD)
prob.ldec[i] = pnorm((x[nx] - forecast$pred[i]) / locSD)
prob.pval[i] = pnorm(abs(x[nx+i] - forecast$pred[i]) / locSD)
}
perf.mape = perf.mape / fx
perf.mse = perf.mse / fx
perf.rmse = sqrt(perf.mse)
bitmap(file='test2.png')
plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$pred
dum[1:12] <- x[(nx+1):lx]
lines(dum, lty=1)
lines(ub,lty=3)
lines(lb,lty=3)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast',9,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'Y[t]',1,header=TRUE)
a<-table.element(a,'F[t]',1,header=TRUE)
a<-table.element(a,'95% LB',1,header=TRUE)
a<-table.element(a,'95% UB',1,header=TRUE)
a<-table.element(a,'p-value
(H0: Y[t] = F[t])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-1])',1,header=TRUE)
a<-table.element(a,'P(F[t]>Y[t-s])',1,header=TRUE)
mylab <- paste('P(F[t]>Y[',nx,sep='')
mylab <- paste(mylab,'])',sep='')
a<-table.element(a,mylab,1,header=TRUE)
a<-table.row.end(a)
for (i in (nx-par5):nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.element(a,'-')
a<-table.row.end(a)
}
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(x[nx+i],4))
a<-table.element(a,round(forecast$pred[i],4))
a<-table.element(a,round(lb[i],4))
a<-table.element(a,round(ub[i],4))
a<-table.element(a,round((1-prob.pval[i]),4))
a<-table.element(a,round((1-prob.dec[i]),4))
a<-table.element(a,round((1-prob.sdec[i]),4))
a<-table.element(a,round((1-prob.ldec[i]),4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Univariate ARIMA Extrapolation Forecast Performance',7,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'time',1,header=TRUE)
a<-table.element(a,'% S.E.',1,header=TRUE)
a<-table.element(a,'PE',1,header=TRUE)
a<-table.element(a,'MAPE',1,header=TRUE)
a<-table.element(a,'Sq.E',1,header=TRUE)
a<-table.element(a,'MSE',1,header=TRUE)
a<-table.element(a,'RMSE',1,header=TRUE)
a<-table.row.end(a)
for (i in 1:fx) {
a<-table.row.start(a)
a<-table.element(a,nx+i,header=TRUE)
a<-table.element(a,round(perc.se[i],4))
a<-table.element(a,round(perf.pe[i],4))
a<-table.element(a,round(perf.mape[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse[i],4))
a<-table.element(a,round(perf.rmse[i],4))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')