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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, 17 Dec 2010 14:53:25 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/17/t1292597503c9cqt4clw8nwbye.htm/, Retrieved Thu, 02 May 2024 12:07:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111501, Retrieved Thu, 02 May 2024 12:07:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact163

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]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
- R PD        [ARIMA Forecasting] [Apple Inc - ARIMA...] [2010-12-17 14:53:25] [aa6b599ccd367bc74fed0d8f67004a46] [Current]
- R PD          [ARIMA Forecasting] [] [2012-12-20 14:10:52] [d1865ed705b6ad9ba3d459a02c528b22]
- R PD          [ARIMA Forecasting] [] [2012-12-20 14:13:43] [d1865ed705b6ad9ba3d459a02c528b22]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10.81
9.12
11.03
12.74
9.98
11.62
9.40
9.27
7.76
8.78
10.65
10.95
12.36
10.85
11.84
12.14
11.65
8.86
7.63
7.38
7.25
8.03
7.75
7.16
7.18
7.51
7.07
7.11
8.98
9.53
10.54
11.31
10.36
11.44
10.45
10.69
11.28
11.96
13.52
12.89
14.03
16.27
16.17
17.25
19.38
26.20
33.53
32.20
38.45
44.86
41.67
36.06
39.76
36.81
42.65
46.89
53.61
57.59
67.82
71.89
75.51
68.49
62.72
70.39
59.77
57.27
67.96
67.85
76.98
81.08
91.66
84.84
85.73
84.61
92.91
99.80
121.19
122.04
131.76
138.48
153.47
189.95
182.22
198.08
135.36
125.02
143.50
173.95
188.75
167.44
158.95
169.53
113.66
107.59
92.67
85.35
90.13
89.31
105.12
125.83
135.81
142.43
163.39
168.21
185.35
188.50
199.91
210.73
192.06
204.62
235.00
261.09
256.88
251.53
257.25
243.10
283.75

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111501&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111501&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111501&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 time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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[105])
93113.66-------
94107.59-------
9592.67-------
9685.35-------
9790.13-------
9889.31-------
99105.12-------
100125.83-------
101135.81-------
102142.43-------
103163.39-------
104168.21-------
105185.35-------
106188.5189.6452141.4433256.53650.48660.55010.99190.5501
107199.91192.7553124.6425304.07390.44990.52990.9610.5519
108210.73194.9934111.9891350.75880.42150.47530.91620.5483
109192.06196.5968101.7033398.12850.48240.44530.84980.5435
110204.62197.741893.0907446.64680.47840.51780.80340.5389
111235198.557785.7579496.52310.40530.48410.73060.5346
112261.09199.138279.44547.89180.36390.42010.65980.5309
113256.88199.550773.9434600.86510.38970.38190.62220.5276
114251.53199.843669.1204655.550.4120.40310.59750.5249
115257.25200.051564.8562712.0540.41330.42190.55580.5224
116243.1200.198961.0596770.48590.44140.42230.54380.5204
117283.75200.303557.6579830.95590.39770.44710.51850.5185

\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[105]) \tabularnewline
93 & 113.66 & - & - & - & - & - & - & - \tabularnewline
94 & 107.59 & - & - & - & - & - & - & - \tabularnewline
95 & 92.67 & - & - & - & - & - & - & - \tabularnewline
96 & 85.35 & - & - & - & - & - & - & - \tabularnewline
97 & 90.13 & - & - & - & - & - & - & - \tabularnewline
98 & 89.31 & - & - & - & - & - & - & - \tabularnewline
99 & 105.12 & - & - & - & - & - & - & - \tabularnewline
100 & 125.83 & - & - & - & - & - & - & - \tabularnewline
101 & 135.81 & - & - & - & - & - & - & - \tabularnewline
102 & 142.43 & - & - & - & - & - & - & - \tabularnewline
103 & 163.39 & - & - & - & - & - & - & - \tabularnewline
104 & 168.21 & - & - & - & - & - & - & - \tabularnewline
105 & 185.35 & - & - & - & - & - & - & - \tabularnewline
106 & 188.5 & 189.6452 & 141.4433 & 256.5365 & 0.4866 & 0.5501 & 0.9919 & 0.5501 \tabularnewline
107 & 199.91 & 192.7553 & 124.6425 & 304.0739 & 0.4499 & 0.5299 & 0.961 & 0.5519 \tabularnewline
108 & 210.73 & 194.9934 & 111.9891 & 350.7588 & 0.4215 & 0.4753 & 0.9162 & 0.5483 \tabularnewline
109 & 192.06 & 196.5968 & 101.7033 & 398.1285 & 0.4824 & 0.4453 & 0.8498 & 0.5435 \tabularnewline
110 & 204.62 & 197.7418 & 93.0907 & 446.6468 & 0.4784 & 0.5178 & 0.8034 & 0.5389 \tabularnewline
111 & 235 & 198.5577 & 85.7579 & 496.5231 & 0.4053 & 0.4841 & 0.7306 & 0.5346 \tabularnewline
112 & 261.09 & 199.1382 & 79.44 & 547.8918 & 0.3639 & 0.4201 & 0.6598 & 0.5309 \tabularnewline
113 & 256.88 & 199.5507 & 73.9434 & 600.8651 & 0.3897 & 0.3819 & 0.6222 & 0.5276 \tabularnewline
114 & 251.53 & 199.8436 & 69.1204 & 655.55 & 0.412 & 0.4031 & 0.5975 & 0.5249 \tabularnewline
115 & 257.25 & 200.0515 & 64.8562 & 712.054 & 0.4133 & 0.4219 & 0.5558 & 0.5224 \tabularnewline
116 & 243.1 & 200.1989 & 61.0596 & 770.4859 & 0.4414 & 0.4223 & 0.5438 & 0.5204 \tabularnewline
117 & 283.75 & 200.3035 & 57.6579 & 830.9559 & 0.3977 & 0.4471 & 0.5185 & 0.5185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111501&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[105])[/C][/ROW]
[ROW][C]93[/C][C]113.66[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]94[/C][C]107.59[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]95[/C][C]92.67[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]96[/C][C]85.35[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]97[/C][C]90.13[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]98[/C][C]89.31[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]99[/C][C]105.12[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]100[/C][C]125.83[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]101[/C][C]135.81[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]102[/C][C]142.43[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]103[/C][C]163.39[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]104[/C][C]168.21[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]105[/C][C]185.35[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]106[/C][C]188.5[/C][C]189.6452[/C][C]141.4433[/C][C]256.5365[/C][C]0.4866[/C][C]0.5501[/C][C]0.9919[/C][C]0.5501[/C][/ROW]
[ROW][C]107[/C][C]199.91[/C][C]192.7553[/C][C]124.6425[/C][C]304.0739[/C][C]0.4499[/C][C]0.5299[/C][C]0.961[/C][C]0.5519[/C][/ROW]
[ROW][C]108[/C][C]210.73[/C][C]194.9934[/C][C]111.9891[/C][C]350.7588[/C][C]0.4215[/C][C]0.4753[/C][C]0.9162[/C][C]0.5483[/C][/ROW]
[ROW][C]109[/C][C]192.06[/C][C]196.5968[/C][C]101.7033[/C][C]398.1285[/C][C]0.4824[/C][C]0.4453[/C][C]0.8498[/C][C]0.5435[/C][/ROW]
[ROW][C]110[/C][C]204.62[/C][C]197.7418[/C][C]93.0907[/C][C]446.6468[/C][C]0.4784[/C][C]0.5178[/C][C]0.8034[/C][C]0.5389[/C][/ROW]
[ROW][C]111[/C][C]235[/C][C]198.5577[/C][C]85.7579[/C][C]496.5231[/C][C]0.4053[/C][C]0.4841[/C][C]0.7306[/C][C]0.5346[/C][/ROW]
[ROW][C]112[/C][C]261.09[/C][C]199.1382[/C][C]79.44[/C][C]547.8918[/C][C]0.3639[/C][C]0.4201[/C][C]0.6598[/C][C]0.5309[/C][/ROW]
[ROW][C]113[/C][C]256.88[/C][C]199.5507[/C][C]73.9434[/C][C]600.8651[/C][C]0.3897[/C][C]0.3819[/C][C]0.6222[/C][C]0.5276[/C][/ROW]
[ROW][C]114[/C][C]251.53[/C][C]199.8436[/C][C]69.1204[/C][C]655.55[/C][C]0.412[/C][C]0.4031[/C][C]0.5975[/C][C]0.5249[/C][/ROW]
[ROW][C]115[/C][C]257.25[/C][C]200.0515[/C][C]64.8562[/C][C]712.054[/C][C]0.4133[/C][C]0.4219[/C][C]0.5558[/C][C]0.5224[/C][/ROW]
[ROW][C]116[/C][C]243.1[/C][C]200.1989[/C][C]61.0596[/C][C]770.4859[/C][C]0.4414[/C][C]0.4223[/C][C]0.5438[/C][C]0.5204[/C][/ROW]
[ROW][C]117[/C][C]283.75[/C][C]200.3035[/C][C]57.6579[/C][C]830.9559[/C][C]0.3977[/C][C]0.4471[/C][C]0.5185[/C][C]0.5185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111501&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111501&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[105])
93113.66-------
94107.59-------
9592.67-------
9685.35-------
9790.13-------
9889.31-------
99105.12-------
100125.83-------
101135.81-------
102142.43-------
103163.39-------
104168.21-------
105185.35-------
106188.5189.6452141.4433256.53650.48660.55010.99190.5501
107199.91192.7553124.6425304.07390.44990.52990.9610.5519
108210.73194.9934111.9891350.75880.42150.47530.91620.5483
109192.06196.5968101.7033398.12850.48240.44530.84980.5435
110204.62197.741893.0907446.64680.47840.51780.80340.5389
111235198.557785.7579496.52310.40530.48410.73060.5346
112261.09199.138279.44547.89180.36390.42010.65980.5309
113256.88199.550773.9434600.86510.38970.38190.62220.5276
114251.53199.843669.1204655.550.4120.40310.59750.5249
115257.25200.051564.8562712.0540.41330.42190.55580.5224
116243.1200.198961.0596770.48590.44140.42230.54380.5204
117283.75200.303557.6579830.95590.39770.44710.51850.5185






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
1060.18-0.00601.311400
1070.29460.03710.021651.189326.25045.1235
1080.40760.08070.0413247.6407100.047110.0024
1090.523-0.02310.036720.582380.18098.9544
1100.64220.03480.036347.309273.60668.5794
1110.76560.18350.06091328.0377282.678416.813
1120.89350.31110.09663838.0236790.584928.1173
1131.02610.28730.12053286.64741102.592733.2053
1141.16340.25860.13582671.48331276.913935.7339
1151.30580.28590.15083271.67281476.389838.4238
1161.45340.21430.15661840.50371509.49138.8522
1171.60640.41660.17836963.32291963.97744.3168

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
106 & 0.18 & -0.006 & 0 & 1.3114 & 0 & 0 \tabularnewline
107 & 0.2946 & 0.0371 & 0.0216 & 51.1893 & 26.2504 & 5.1235 \tabularnewline
108 & 0.4076 & 0.0807 & 0.0413 & 247.6407 & 100.0471 & 10.0024 \tabularnewline
109 & 0.523 & -0.0231 & 0.0367 & 20.5823 & 80.1809 & 8.9544 \tabularnewline
110 & 0.6422 & 0.0348 & 0.0363 & 47.3092 & 73.6066 & 8.5794 \tabularnewline
111 & 0.7656 & 0.1835 & 0.0609 & 1328.0377 & 282.6784 & 16.813 \tabularnewline
112 & 0.8935 & 0.3111 & 0.0966 & 3838.0236 & 790.5849 & 28.1173 \tabularnewline
113 & 1.0261 & 0.2873 & 0.1205 & 3286.6474 & 1102.5927 & 33.2053 \tabularnewline
114 & 1.1634 & 0.2586 & 0.1358 & 2671.4833 & 1276.9139 & 35.7339 \tabularnewline
115 & 1.3058 & 0.2859 & 0.1508 & 3271.6728 & 1476.3898 & 38.4238 \tabularnewline
116 & 1.4534 & 0.2143 & 0.1566 & 1840.5037 & 1509.491 & 38.8522 \tabularnewline
117 & 1.6064 & 0.4166 & 0.1783 & 6963.3229 & 1963.977 & 44.3168 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111501&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]106[/C][C]0.18[/C][C]-0.006[/C][C]0[/C][C]1.3114[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]0.2946[/C][C]0.0371[/C][C]0.0216[/C][C]51.1893[/C][C]26.2504[/C][C]5.1235[/C][/ROW]
[ROW][C]108[/C][C]0.4076[/C][C]0.0807[/C][C]0.0413[/C][C]247.6407[/C][C]100.0471[/C][C]10.0024[/C][/ROW]
[ROW][C]109[/C][C]0.523[/C][C]-0.0231[/C][C]0.0367[/C][C]20.5823[/C][C]80.1809[/C][C]8.9544[/C][/ROW]
[ROW][C]110[/C][C]0.6422[/C][C]0.0348[/C][C]0.0363[/C][C]47.3092[/C][C]73.6066[/C][C]8.5794[/C][/ROW]
[ROW][C]111[/C][C]0.7656[/C][C]0.1835[/C][C]0.0609[/C][C]1328.0377[/C][C]282.6784[/C][C]16.813[/C][/ROW]
[ROW][C]112[/C][C]0.8935[/C][C]0.3111[/C][C]0.0966[/C][C]3838.0236[/C][C]790.5849[/C][C]28.1173[/C][/ROW]
[ROW][C]113[/C][C]1.0261[/C][C]0.2873[/C][C]0.1205[/C][C]3286.6474[/C][C]1102.5927[/C][C]33.2053[/C][/ROW]
[ROW][C]114[/C][C]1.1634[/C][C]0.2586[/C][C]0.1358[/C][C]2671.4833[/C][C]1276.9139[/C][C]35.7339[/C][/ROW]
[ROW][C]115[/C][C]1.3058[/C][C]0.2859[/C][C]0.1508[/C][C]3271.6728[/C][C]1476.3898[/C][C]38.4238[/C][/ROW]
[ROW][C]116[/C][C]1.4534[/C][C]0.2143[/C][C]0.1566[/C][C]1840.5037[/C][C]1509.491[/C][C]38.8522[/C][/ROW]
[ROW][C]117[/C][C]1.6064[/C][C]0.4166[/C][C]0.1783[/C][C]6963.3229[/C][C]1963.977[/C][C]44.3168[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111501&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111501&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
1060.18-0.00601.311400
1070.29460.03710.021651.189326.25045.1235
1080.40760.08070.0413247.6407100.047110.0024
1090.523-0.02310.036720.582380.18098.9544
1100.64220.03480.036347.309273.60668.5794
1110.76560.18350.06091328.0377282.678416.813
1120.89350.31110.09663838.0236790.584928.1173
1131.02610.28730.12053286.64741102.592733.2053
1141.16340.25860.13582671.48331276.913935.7339
1151.30580.28590.15083271.67281476.389838.4238
1161.45340.21430.15661840.50371509.49138.8522
1171.60640.41660.17836963.32291963.97744.3168


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = -0.1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = -0.1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 1 ; 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,par1))
(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.mape1 <- array(0, dim=fx)
perf.se <- array(0, dim=fx)
perf.mse <- array(0, dim=fx)
perf.mse1 <- 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$pred[i]
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
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[1] = abs(perf.pe[1])
perf.mse[1] = abs(perf.se[1])
for (i in 2:fx) {
perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])
perf.mape1[i] = perf.mape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
}
perf.rmse = sqrt(perf.mse1)
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:par1] <- 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.mape1[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse1[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')