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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 computationSun, 21 Dec 2008 13:01:58 -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/2008/Dec/21/t1229889804ekbvo08qn7go3ns.htm/, Retrieved Fri, 09 May 2025 15:14:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35800, Retrieved Fri, 09 May 2025 15:14:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact307

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
F    D  [Multiple Regression] [The Seatbelt Law ...] [2008-11-15 12:00:57] [93834488277b53a4510bfd06084ae13b]
-   PD    [Multiple Regression] [] [2008-11-15 18:50:27] [93834488277b53a4510bfd06084ae13b]
-    D      [Multiple Regression] [Paper - Multiple ...] [2008-12-21 14:45:31] [85841a4a203c2f9589565c024425a91b]
-   PD        [Multiple Regression] [Paper - Multiple ...] [2008-12-21 15:09:11] [85841a4a203c2f9589565c024425a91b]
- RMPD            [ARIMA Forecasting] [Paper - Arima for...] [2008-12-21 20:01:58] [07b7cf1321bc38017c2c7efcf91ca696] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.57
97.74
97.92
98.19
98.23
98.41
98.59
98.71
99.14
99.62
100.18
100.66
101.19
101.75
102.2
102.87
98.81
97.6
96.68
95.96
98.89
99.05
99.2
99.11
99.19
99.77
100.70
100.78
100.53
101.01
100.92
101.10
103.11
102.99
102.31
102.61
103.68
104.72
107.66
108.87
108.12
107.61
106.42
105.61
105.71
105.49
105.57
105.18
106.09
106.34
108.47
116.87
121.08
123.27
124.18
125.60
126.57
127.18
128.04
128.55
129.67

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35800&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]1 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=35800&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35800&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 time1 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[49])
37103.68-------
38104.72-------
39107.66-------
40108.87-------
41108.12-------
42107.61-------
43106.42-------
44105.61-------
45105.71-------
46105.49-------
47105.57-------
48105.18-------
49106.09-------
50106.34106.3087104.0815108.68540.48970.57160.90490.5716
51108.47106.3524102.8758110.20720.14080.50250.25310.5531
52116.87106.3619101.9558111.393500.20580.16430.5422
53121.08106.3638101.2079112.397703e-040.28420.5354
54123.27106.3642100.5713113.2894000.36220.5309
55124.18106.3643100.0119114.1045000.49440.5277
56125.6106.364399.5095114.8638000.5690.5252
57126.57106.364399.0513115.5801000.55530.5233
58127.18106.364398.6285116.2623000.56870.5217
59128.04106.364398.2348116.916401e-040.55860.5203
60128.55106.364397.8657117.54721e-041e-040.58220.5192
61129.67106.364397.5177118.15821e-041e-040.51820.5182

\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[49]) \tabularnewline
37 & 103.68 & - & - & - & - & - & - & - \tabularnewline
38 & 104.72 & - & - & - & - & - & - & - \tabularnewline
39 & 107.66 & - & - & - & - & - & - & - \tabularnewline
40 & 108.87 & - & - & - & - & - & - & - \tabularnewline
41 & 108.12 & - & - & - & - & - & - & - \tabularnewline
42 & 107.61 & - & - & - & - & - & - & - \tabularnewline
43 & 106.42 & - & - & - & - & - & - & - \tabularnewline
44 & 105.61 & - & - & - & - & - & - & - \tabularnewline
45 & 105.71 & - & - & - & - & - & - & - \tabularnewline
46 & 105.49 & - & - & - & - & - & - & - \tabularnewline
47 & 105.57 & - & - & - & - & - & - & - \tabularnewline
48 & 105.18 & - & - & - & - & - & - & - \tabularnewline
49 & 106.09 & - & - & - & - & - & - & - \tabularnewline
50 & 106.34 & 106.3087 & 104.0815 & 108.6854 & 0.4897 & 0.5716 & 0.9049 & 0.5716 \tabularnewline
51 & 108.47 & 106.3524 & 102.8758 & 110.2072 & 0.1408 & 0.5025 & 0.2531 & 0.5531 \tabularnewline
52 & 116.87 & 106.3619 & 101.9558 & 111.3935 & 0 & 0.2058 & 0.1643 & 0.5422 \tabularnewline
53 & 121.08 & 106.3638 & 101.2079 & 112.3977 & 0 & 3e-04 & 0.2842 & 0.5354 \tabularnewline
54 & 123.27 & 106.3642 & 100.5713 & 113.2894 & 0 & 0 & 0.3622 & 0.5309 \tabularnewline
55 & 124.18 & 106.3643 & 100.0119 & 114.1045 & 0 & 0 & 0.4944 & 0.5277 \tabularnewline
56 & 125.6 & 106.3643 & 99.5095 & 114.8638 & 0 & 0 & 0.569 & 0.5252 \tabularnewline
57 & 126.57 & 106.3643 & 99.0513 & 115.5801 & 0 & 0 & 0.5553 & 0.5233 \tabularnewline
58 & 127.18 & 106.3643 & 98.6285 & 116.2623 & 0 & 0 & 0.5687 & 0.5217 \tabularnewline
59 & 128.04 & 106.3643 & 98.2348 & 116.9164 & 0 & 1e-04 & 0.5586 & 0.5203 \tabularnewline
60 & 128.55 & 106.3643 & 97.8657 & 117.5472 & 1e-04 & 1e-04 & 0.5822 & 0.5192 \tabularnewline
61 & 129.67 & 106.3643 & 97.5177 & 118.1582 & 1e-04 & 1e-04 & 0.5182 & 0.5182 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35800&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[49])[/C][/ROW]
[ROW][C]37[/C][C]103.68[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]104.72[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]107.66[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]108.87[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]108.12[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]107.61[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]106.42[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]105.61[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]105.71[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]105.49[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]105.57[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]105.18[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]106.09[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]106.34[/C][C]106.3087[/C][C]104.0815[/C][C]108.6854[/C][C]0.4897[/C][C]0.5716[/C][C]0.9049[/C][C]0.5716[/C][/ROW]
[ROW][C]51[/C][C]108.47[/C][C]106.3524[/C][C]102.8758[/C][C]110.2072[/C][C]0.1408[/C][C]0.5025[/C][C]0.2531[/C][C]0.5531[/C][/ROW]
[ROW][C]52[/C][C]116.87[/C][C]106.3619[/C][C]101.9558[/C][C]111.3935[/C][C]0[/C][C]0.2058[/C][C]0.1643[/C][C]0.5422[/C][/ROW]
[ROW][C]53[/C][C]121.08[/C][C]106.3638[/C][C]101.2079[/C][C]112.3977[/C][C]0[/C][C]3e-04[/C][C]0.2842[/C][C]0.5354[/C][/ROW]
[ROW][C]54[/C][C]123.27[/C][C]106.3642[/C][C]100.5713[/C][C]113.2894[/C][C]0[/C][C]0[/C][C]0.3622[/C][C]0.5309[/C][/ROW]
[ROW][C]55[/C][C]124.18[/C][C]106.3643[/C][C]100.0119[/C][C]114.1045[/C][C]0[/C][C]0[/C][C]0.4944[/C][C]0.5277[/C][/ROW]
[ROW][C]56[/C][C]125.6[/C][C]106.3643[/C][C]99.5095[/C][C]114.8638[/C][C]0[/C][C]0[/C][C]0.569[/C][C]0.5252[/C][/ROW]
[ROW][C]57[/C][C]126.57[/C][C]106.3643[/C][C]99.0513[/C][C]115.5801[/C][C]0[/C][C]0[/C][C]0.5553[/C][C]0.5233[/C][/ROW]
[ROW][C]58[/C][C]127.18[/C][C]106.3643[/C][C]98.6285[/C][C]116.2623[/C][C]0[/C][C]0[/C][C]0.5687[/C][C]0.5217[/C][/ROW]
[ROW][C]59[/C][C]128.04[/C][C]106.3643[/C][C]98.2348[/C][C]116.9164[/C][C]0[/C][C]1e-04[/C][C]0.5586[/C][C]0.5203[/C][/ROW]
[ROW][C]60[/C][C]128.55[/C][C]106.3643[/C][C]97.8657[/C][C]117.5472[/C][C]1e-04[/C][C]1e-04[/C][C]0.5822[/C][C]0.5192[/C][/ROW]
[ROW][C]61[/C][C]129.67[/C][C]106.3643[/C][C]97.5177[/C][C]118.1582[/C][C]1e-04[/C][C]1e-04[/C][C]0.5182[/C][C]0.5182[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35800&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35800&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[49])
37103.68-------
38104.72-------
39107.66-------
40108.87-------
41108.12-------
42107.61-------
43106.42-------
44105.61-------
45105.71-------
46105.49-------
47105.57-------
48105.18-------
49106.09-------
50106.34106.3087104.0815108.68540.48970.57160.90490.5716
51108.47106.3524102.8758110.20720.14080.50250.25310.5531
52116.87106.3619101.9558111.393500.20580.16430.5422
53121.08106.3638101.2079112.397703e-040.28420.5354
54123.27106.3642100.5713113.2894000.36220.5309
55124.18106.3643100.0119114.1045000.49440.5277
56125.6106.364399.5095114.8638000.5690.5252
57126.57106.364399.0513115.5801000.55530.5233
58127.18106.364398.6285116.2623000.56870.5217
59128.04106.364398.2348116.916401e-040.55860.5203
60128.55106.364397.8657117.54721e-041e-040.58220.5192
61129.67106.364397.5177118.15821e-041e-040.51820.5182






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
500.01143e-0400.0011e-040.009
510.01850.01990.00174.48410.37370.6113
520.02410.09880.0082110.4219.20183.0334
530.02890.13840.0115216.566118.04724.2482
540.03320.15890.0132285.805123.81714.8803
550.03710.16750.014317.398626.44995.1429
560.04080.18080.0151370.010830.83425.5529
570.04420.190.0158408.268834.02245.8329
580.04750.19570.0163433.291736.10766.009
590.05060.20380.017469.834339.15296.2572
600.05360.20860.0174492.203541.0176.4044
610.05660.21910.0183543.153845.26286.7278

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
50 & 0.0114 & 3e-04 & 0 & 0.001 & 1e-04 & 0.009 \tabularnewline
51 & 0.0185 & 0.0199 & 0.0017 & 4.4841 & 0.3737 & 0.6113 \tabularnewline
52 & 0.0241 & 0.0988 & 0.0082 & 110.421 & 9.2018 & 3.0334 \tabularnewline
53 & 0.0289 & 0.1384 & 0.0115 & 216.5661 & 18.0472 & 4.2482 \tabularnewline
54 & 0.0332 & 0.1589 & 0.0132 & 285.8051 & 23.8171 & 4.8803 \tabularnewline
55 & 0.0371 & 0.1675 & 0.014 & 317.3986 & 26.4499 & 5.1429 \tabularnewline
56 & 0.0408 & 0.1808 & 0.0151 & 370.0108 & 30.8342 & 5.5529 \tabularnewline
57 & 0.0442 & 0.19 & 0.0158 & 408.2688 & 34.0224 & 5.8329 \tabularnewline
58 & 0.0475 & 0.1957 & 0.0163 & 433.2917 & 36.1076 & 6.009 \tabularnewline
59 & 0.0506 & 0.2038 & 0.017 & 469.8343 & 39.1529 & 6.2572 \tabularnewline
60 & 0.0536 & 0.2086 & 0.0174 & 492.2035 & 41.017 & 6.4044 \tabularnewline
61 & 0.0566 & 0.2191 & 0.0183 & 543.1538 & 45.2628 & 6.7278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35800&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]50[/C][C]0.0114[/C][C]3e-04[/C][C]0[/C][C]0.001[/C][C]1e-04[/C][C]0.009[/C][/ROW]
[ROW][C]51[/C][C]0.0185[/C][C]0.0199[/C][C]0.0017[/C][C]4.4841[/C][C]0.3737[/C][C]0.6113[/C][/ROW]
[ROW][C]52[/C][C]0.0241[/C][C]0.0988[/C][C]0.0082[/C][C]110.421[/C][C]9.2018[/C][C]3.0334[/C][/ROW]
[ROW][C]53[/C][C]0.0289[/C][C]0.1384[/C][C]0.0115[/C][C]216.5661[/C][C]18.0472[/C][C]4.2482[/C][/ROW]
[ROW][C]54[/C][C]0.0332[/C][C]0.1589[/C][C]0.0132[/C][C]285.8051[/C][C]23.8171[/C][C]4.8803[/C][/ROW]
[ROW][C]55[/C][C]0.0371[/C][C]0.1675[/C][C]0.014[/C][C]317.3986[/C][C]26.4499[/C][C]5.1429[/C][/ROW]
[ROW][C]56[/C][C]0.0408[/C][C]0.1808[/C][C]0.0151[/C][C]370.0108[/C][C]30.8342[/C][C]5.5529[/C][/ROW]
[ROW][C]57[/C][C]0.0442[/C][C]0.19[/C][C]0.0158[/C][C]408.2688[/C][C]34.0224[/C][C]5.8329[/C][/ROW]
[ROW][C]58[/C][C]0.0475[/C][C]0.1957[/C][C]0.0163[/C][C]433.2917[/C][C]36.1076[/C][C]6.009[/C][/ROW]
[ROW][C]59[/C][C]0.0506[/C][C]0.2038[/C][C]0.017[/C][C]469.8343[/C][C]39.1529[/C][C]6.2572[/C][/ROW]
[ROW][C]60[/C][C]0.0536[/C][C]0.2086[/C][C]0.0174[/C][C]492.2035[/C][C]41.017[/C][C]6.4044[/C][/ROW]
[ROW][C]61[/C][C]0.0566[/C][C]0.2191[/C][C]0.0183[/C][C]543.1538[/C][C]45.2628[/C][C]6.7278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35800&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35800&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
500.01143e-0400.0011e-040.009
510.01850.01990.00174.48410.37370.6113
520.02410.09880.0082110.4219.20183.0334
530.02890.13840.0115216.566118.04724.2482
540.03320.15890.0132285.805123.81714.8803
550.03710.16750.014317.398626.44995.1429
560.04080.18080.0151370.010830.83425.5529
570.04420.190.0158408.268834.02245.8329
580.04750.19570.0163433.291736.10766.009
590.05060.20380.017469.834339.15296.2572
600.05360.20860.0174492.203541.0176.4044
610.05660.21910.0183543.153845.26286.7278


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = -2.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = -2.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; 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')