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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 computationTue, 21 Dec 2010 14:41:20 +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/21/t1292942348i7iuqkmpf3duihh.htm/, Retrieved Wed, 15 May 2024 10:48:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113625, Retrieved Wed, 15 May 2024 10:48:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

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]
-   PD      [ARIMA Forecasting] [test 7] [2010-12-05 11:13:06] [74be16979710d4c4e7c6647856088456]
-   P         [ARIMA Forecasting] [W9 - Blog 9] [2010-12-06 16:23:47] [1aa8d85d6b335d32b1f6be940e33a166]
-   PD          [ARIMA Forecasting] [ARIMA forecast Li...] [2010-12-20 15:06:20] [1aa8d85d6b335d32b1f6be940e33a166]
-   PD              [ARIMA Forecasting] [ARIMA forecast Wh...] [2010-12-21 14:41:20] [47bfda5353cd53c1cf7ea7aa9038654a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
16,46
16,49
16,59
16,58
16,60
16,55
16,57
16,51
16,50
16,49
16,44
16,26
16,33
16,72
16,75
16,74
16,84
16,79
16,66
16,69
16,84
16,86
16,76
16,72
16,29
16,29
16,46
16,54
16,70
16,82
16,88
16,89
16,92
16,88
16,91
16,80
16,78
17,03
17,18
17,12
17,11
17,14
17,17
17,21
17,22
17,19
17,15
17,10
17,21
17,33
17,30
17,33
17,35
17,43
17,46
17,50
17,54
17,56
17,44
17,41
17,72
17,79
17,83
17,76
17,95
17,91
17,96
17,98
17,89
17,88
17,91
17,51
17,63

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=113625&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=113625&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113625&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[61])
4917.21-------
5017.33-------
5117.3-------
5217.33-------
5317.35-------
5417.43-------
5517.46-------
5617.5-------
5717.54-------
5817.56-------
5917.44-------
6017.41-------
6117.72-------
6217.7917.7217.493117.94690.27270.50.99960.5
6317.8317.7217.399218.04080.25080.33450.99490.5
6417.7617.7217.327118.11290.42090.29160.97410.5
6517.9517.7217.266318.17370.16020.43140.9450.5
6617.9117.7217.212718.22730.23140.18710.86880.5
6717.9617.7217.164318.27570.19860.25140.82040.5
6817.9817.7217.119818.32020.19790.21660.76370.5
6917.8917.7217.078318.36170.30180.21350.70880.5
7017.8817.7217.039418.40060.32250.31220.67750.5
7117.9117.7217.002618.43740.30180.3310.77790.5
7217.5117.7216.967618.47240.29220.31030.79030.5
7317.6317.7216.934118.50590.41120.69980.50.5

\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[61]) \tabularnewline
49 & 17.21 & - & - & - & - & - & - & - \tabularnewline
50 & 17.33 & - & - & - & - & - & - & - \tabularnewline
51 & 17.3 & - & - & - & - & - & - & - \tabularnewline
52 & 17.33 & - & - & - & - & - & - & - \tabularnewline
53 & 17.35 & - & - & - & - & - & - & - \tabularnewline
54 & 17.43 & - & - & - & - & - & - & - \tabularnewline
55 & 17.46 & - & - & - & - & - & - & - \tabularnewline
56 & 17.5 & - & - & - & - & - & - & - \tabularnewline
57 & 17.54 & - & - & - & - & - & - & - \tabularnewline
58 & 17.56 & - & - & - & - & - & - & - \tabularnewline
59 & 17.44 & - & - & - & - & - & - & - \tabularnewline
60 & 17.41 & - & - & - & - & - & - & - \tabularnewline
61 & 17.72 & - & - & - & - & - & - & - \tabularnewline
62 & 17.79 & 17.72 & 17.4931 & 17.9469 & 0.2727 & 0.5 & 0.9996 & 0.5 \tabularnewline
63 & 17.83 & 17.72 & 17.3992 & 18.0408 & 0.2508 & 0.3345 & 0.9949 & 0.5 \tabularnewline
64 & 17.76 & 17.72 & 17.3271 & 18.1129 & 0.4209 & 0.2916 & 0.9741 & 0.5 \tabularnewline
65 & 17.95 & 17.72 & 17.2663 & 18.1737 & 0.1602 & 0.4314 & 0.945 & 0.5 \tabularnewline
66 & 17.91 & 17.72 & 17.2127 & 18.2273 & 0.2314 & 0.1871 & 0.8688 & 0.5 \tabularnewline
67 & 17.96 & 17.72 & 17.1643 & 18.2757 & 0.1986 & 0.2514 & 0.8204 & 0.5 \tabularnewline
68 & 17.98 & 17.72 & 17.1198 & 18.3202 & 0.1979 & 0.2166 & 0.7637 & 0.5 \tabularnewline
69 & 17.89 & 17.72 & 17.0783 & 18.3617 & 0.3018 & 0.2135 & 0.7088 & 0.5 \tabularnewline
70 & 17.88 & 17.72 & 17.0394 & 18.4006 & 0.3225 & 0.3122 & 0.6775 & 0.5 \tabularnewline
71 & 17.91 & 17.72 & 17.0026 & 18.4374 & 0.3018 & 0.331 & 0.7779 & 0.5 \tabularnewline
72 & 17.51 & 17.72 & 16.9676 & 18.4724 & 0.2922 & 0.3103 & 0.7903 & 0.5 \tabularnewline
73 & 17.63 & 17.72 & 16.9341 & 18.5059 & 0.4112 & 0.6998 & 0.5 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113625&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[61])[/C][/ROW]
[ROW][C]49[/C][C]17.21[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]17.33[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]51[/C][C]17.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]52[/C][C]17.33[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]53[/C][C]17.35[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]54[/C][C]17.43[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]55[/C][C]17.46[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]56[/C][C]17.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]57[/C][C]17.54[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]58[/C][C]17.56[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]59[/C][C]17.44[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]60[/C][C]17.41[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]17.72[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]62[/C][C]17.79[/C][C]17.72[/C][C]17.4931[/C][C]17.9469[/C][C]0.2727[/C][C]0.5[/C][C]0.9996[/C][C]0.5[/C][/ROW]
[ROW][C]63[/C][C]17.83[/C][C]17.72[/C][C]17.3992[/C][C]18.0408[/C][C]0.2508[/C][C]0.3345[/C][C]0.9949[/C][C]0.5[/C][/ROW]
[ROW][C]64[/C][C]17.76[/C][C]17.72[/C][C]17.3271[/C][C]18.1129[/C][C]0.4209[/C][C]0.2916[/C][C]0.9741[/C][C]0.5[/C][/ROW]
[ROW][C]65[/C][C]17.95[/C][C]17.72[/C][C]17.2663[/C][C]18.1737[/C][C]0.1602[/C][C]0.4314[/C][C]0.945[/C][C]0.5[/C][/ROW]
[ROW][C]66[/C][C]17.91[/C][C]17.72[/C][C]17.2127[/C][C]18.2273[/C][C]0.2314[/C][C]0.1871[/C][C]0.8688[/C][C]0.5[/C][/ROW]
[ROW][C]67[/C][C]17.96[/C][C]17.72[/C][C]17.1643[/C][C]18.2757[/C][C]0.1986[/C][C]0.2514[/C][C]0.8204[/C][C]0.5[/C][/ROW]
[ROW][C]68[/C][C]17.98[/C][C]17.72[/C][C]17.1198[/C][C]18.3202[/C][C]0.1979[/C][C]0.2166[/C][C]0.7637[/C][C]0.5[/C][/ROW]
[ROW][C]69[/C][C]17.89[/C][C]17.72[/C][C]17.0783[/C][C]18.3617[/C][C]0.3018[/C][C]0.2135[/C][C]0.7088[/C][C]0.5[/C][/ROW]
[ROW][C]70[/C][C]17.88[/C][C]17.72[/C][C]17.0394[/C][C]18.4006[/C][C]0.3225[/C][C]0.3122[/C][C]0.6775[/C][C]0.5[/C][/ROW]
[ROW][C]71[/C][C]17.91[/C][C]17.72[/C][C]17.0026[/C][C]18.4374[/C][C]0.3018[/C][C]0.331[/C][C]0.7779[/C][C]0.5[/C][/ROW]
[ROW][C]72[/C][C]17.51[/C][C]17.72[/C][C]16.9676[/C][C]18.4724[/C][C]0.2922[/C][C]0.3103[/C][C]0.7903[/C][C]0.5[/C][/ROW]
[ROW][C]73[/C][C]17.63[/C][C]17.72[/C][C]16.9341[/C][C]18.5059[/C][C]0.4112[/C][C]0.6998[/C][C]0.5[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113625&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113625&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[61])
4917.21-------
5017.33-------
5117.3-------
5217.33-------
5317.35-------
5417.43-------
5517.46-------
5617.5-------
5717.54-------
5817.56-------
5917.44-------
6017.41-------
6117.72-------
6217.7917.7217.493117.94690.27270.50.99960.5
6317.8317.7217.399218.04080.25080.33450.99490.5
6417.7617.7217.327118.11290.42090.29160.97410.5
6517.9517.7217.266318.17370.16020.43140.9450.5
6617.9117.7217.212718.22730.23140.18710.86880.5
6717.9617.7217.164318.27570.19860.25140.82040.5
6817.9817.7217.119818.32020.19790.21660.76370.5
6917.8917.7217.078318.36170.30180.21350.70880.5
7017.8817.7217.039418.40060.32250.31220.67750.5
7117.9117.7217.002618.43740.30180.3310.77790.5
7217.5117.7216.967618.47240.29220.31030.79030.5
7317.6317.7216.934118.50590.41120.69980.50.5






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
620.00650.00400.004900
630.00920.00620.00510.01210.00850.0922
640.01130.00230.00410.00160.00620.0787
650.01310.0130.00630.05290.01790.1337
660.01460.01070.00720.03610.02150.1467
670.0160.01350.00830.05760.02750.1659
680.01730.01470.00920.06760.03330.1824
690.01850.00960.00920.02890.03270.1809
700.01960.0090.00920.02560.03190.1787
710.02070.01070.00940.03610.03230.1798
720.0217-0.01190.00960.04410.03340.1828
730.0226-0.00510.00920.00810.03130.1769

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
62 & 0.0065 & 0.004 & 0 & 0.0049 & 0 & 0 \tabularnewline
63 & 0.0092 & 0.0062 & 0.0051 & 0.0121 & 0.0085 & 0.0922 \tabularnewline
64 & 0.0113 & 0.0023 & 0.0041 & 0.0016 & 0.0062 & 0.0787 \tabularnewline
65 & 0.0131 & 0.013 & 0.0063 & 0.0529 & 0.0179 & 0.1337 \tabularnewline
66 & 0.0146 & 0.0107 & 0.0072 & 0.0361 & 0.0215 & 0.1467 \tabularnewline
67 & 0.016 & 0.0135 & 0.0083 & 0.0576 & 0.0275 & 0.1659 \tabularnewline
68 & 0.0173 & 0.0147 & 0.0092 & 0.0676 & 0.0333 & 0.1824 \tabularnewline
69 & 0.0185 & 0.0096 & 0.0092 & 0.0289 & 0.0327 & 0.1809 \tabularnewline
70 & 0.0196 & 0.009 & 0.0092 & 0.0256 & 0.0319 & 0.1787 \tabularnewline
71 & 0.0207 & 0.0107 & 0.0094 & 0.0361 & 0.0323 & 0.1798 \tabularnewline
72 & 0.0217 & -0.0119 & 0.0096 & 0.0441 & 0.0334 & 0.1828 \tabularnewline
73 & 0.0226 & -0.0051 & 0.0092 & 0.0081 & 0.0313 & 0.1769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113625&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]62[/C][C]0.0065[/C][C]0.004[/C][C]0[/C][C]0.0049[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]63[/C][C]0.0092[/C][C]0.0062[/C][C]0.0051[/C][C]0.0121[/C][C]0.0085[/C][C]0.0922[/C][/ROW]
[ROW][C]64[/C][C]0.0113[/C][C]0.0023[/C][C]0.0041[/C][C]0.0016[/C][C]0.0062[/C][C]0.0787[/C][/ROW]
[ROW][C]65[/C][C]0.0131[/C][C]0.013[/C][C]0.0063[/C][C]0.0529[/C][C]0.0179[/C][C]0.1337[/C][/ROW]
[ROW][C]66[/C][C]0.0146[/C][C]0.0107[/C][C]0.0072[/C][C]0.0361[/C][C]0.0215[/C][C]0.1467[/C][/ROW]
[ROW][C]67[/C][C]0.016[/C][C]0.0135[/C][C]0.0083[/C][C]0.0576[/C][C]0.0275[/C][C]0.1659[/C][/ROW]
[ROW][C]68[/C][C]0.0173[/C][C]0.0147[/C][C]0.0092[/C][C]0.0676[/C][C]0.0333[/C][C]0.1824[/C][/ROW]
[ROW][C]69[/C][C]0.0185[/C][C]0.0096[/C][C]0.0092[/C][C]0.0289[/C][C]0.0327[/C][C]0.1809[/C][/ROW]
[ROW][C]70[/C][C]0.0196[/C][C]0.009[/C][C]0.0092[/C][C]0.0256[/C][C]0.0319[/C][C]0.1787[/C][/ROW]
[ROW][C]71[/C][C]0.0207[/C][C]0.0107[/C][C]0.0094[/C][C]0.0361[/C][C]0.0323[/C][C]0.1798[/C][/ROW]
[ROW][C]72[/C][C]0.0217[/C][C]-0.0119[/C][C]0.0096[/C][C]0.0441[/C][C]0.0334[/C][C]0.1828[/C][/ROW]
[ROW][C]73[/C][C]0.0226[/C][C]-0.0051[/C][C]0.0092[/C][C]0.0081[/C][C]0.0313[/C][C]0.1769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113625&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113625&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
620.00650.00400.004900
630.00920.00620.00510.01210.00850.0922
640.01130.00230.00410.00160.00620.0787
650.01310.0130.00630.05290.01790.1337
660.01460.01070.00720.03610.02150.1467
670.0160.01350.00830.05760.02750.1659
680.01730.01470.00920.06760.03330.1824
690.01850.00960.00920.02890.03270.1809
700.01960.0090.00920.02560.03190.1787
710.02070.01070.00940.03610.03230.1798
720.0217-0.01190.00960.04410.03340.1828
730.0226-0.00510.00920.00810.03130.1769


Figures (Output of Computation)

Input Parameters & R Code

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