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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationThu, 20 Dec 2012 06:20:35 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/20/t13560024453p5am67gy5t77fm.htm/, Retrieved Thu, 28 Mar 2024 21:56:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202619, Retrieved Thu, 28 Mar 2024 21:56:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact91
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] [ARIMA Forecasting] [2012-12-04 18:38:36] [f055db2f1c47e4197bf514e64f7886e5]
- R P           [ARIMA Forecasting] [Paper2012: ARIMA] [2012-12-20 11:20:35] [86f0addf4b5362ca5a545029cdfac14b] [Current]
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Post a new message
Dataseries X:
46
62
66
59
58
61
41
27
58
70
49
59
44
36
72
45
56
54
53
35
61
52
47
51
52
63
74
45
51
64
36
30
55
64
39
40
63
45
59
55
40
64
27
28
45
57
45
69
60
56
58
50
51
53
37
22
55
70
62
58
39
49
58
47
42
62
39
40
72
70
54
65




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202619&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202619&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202619&T=0

As an alternative you can also use a 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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







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[60])
4869-------
4960-------
5056-------
5158-------
5250-------
5351-------
5453-------
5537-------
5622-------
5755-------
5870-------
5962-------
6058-------
613964.011345.01183.01160.00490.73240.66050.7324
624949.64230.632368.65170.47360.86370.25610.1944
635859.188439.97978.39780.45170.85070.54830.5483
644752.136832.873471.40020.30060.27540.58610.2754
654245.658626.365564.95160.35510.44580.29370.105
666258.437139.130877.74350.35880.95240.70950.5177
673932.011712.699151.32430.23910.00120.30630.0042
684024.98145.665944.2970.06380.07740.61894e-04
697249.937730.620869.25460.01260.84340.30370.2067
707063.290443.972882.60790.2480.18840.2480.7043
715453.314133.996372.6320.47230.04520.18910.3172
726563.619244.301282.93710.44430.83550.71570.7157

\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[60]) \tabularnewline
48 & 69 & - & - & - & - & - & - & - \tabularnewline
49 & 60 & - & - & - & - & - & - & - \tabularnewline
50 & 56 & - & - & - & - & - & - & - \tabularnewline
51 & 58 & - & - & - & - & - & - & - \tabularnewline
52 & 50 & - & - & - & - & - & - & - \tabularnewline
53 & 51 & - & - & - & - & - & - & - \tabularnewline
54 & 53 & - & - & - & - & - & - & - \tabularnewline
55 & 37 & - & - & - & - & - & - & - \tabularnewline
56 & 22 & - & - & - & - & - & - & - \tabularnewline
57 & 55 & - & - & - & - & - & - & - \tabularnewline
58 & 70 & - & - & - & - & - & - & - \tabularnewline
59 & 62 & - & - & - & - & - & - & - \tabularnewline
60 & 58 & - & - & - & - & - & - & - \tabularnewline
61 & 39 & 64.0113 & 45.011 & 83.0116 & 0.0049 & 0.7324 & 0.6605 & 0.7324 \tabularnewline
62 & 49 & 49.642 & 30.6323 & 68.6517 & 0.4736 & 0.8637 & 0.2561 & 0.1944 \tabularnewline
63 & 58 & 59.1884 & 39.979 & 78.3978 & 0.4517 & 0.8507 & 0.5483 & 0.5483 \tabularnewline
64 & 47 & 52.1368 & 32.8734 & 71.4002 & 0.3006 & 0.2754 & 0.5861 & 0.2754 \tabularnewline
65 & 42 & 45.6586 & 26.3655 & 64.9516 & 0.3551 & 0.4458 & 0.2937 & 0.105 \tabularnewline
66 & 62 & 58.4371 & 39.1308 & 77.7435 & 0.3588 & 0.9524 & 0.7095 & 0.5177 \tabularnewline
67 & 39 & 32.0117 & 12.6991 & 51.3243 & 0.2391 & 0.0012 & 0.3063 & 0.0042 \tabularnewline
68 & 40 & 24.9814 & 5.6659 & 44.297 & 0.0638 & 0.0774 & 0.6189 & 4e-04 \tabularnewline
69 & 72 & 49.9377 & 30.6208 & 69.2546 & 0.0126 & 0.8434 & 0.3037 & 0.2067 \tabularnewline
70 & 70 & 63.2904 & 43.9728 & 82.6079 & 0.248 & 0.1884 & 0.248 & 0.7043 \tabularnewline
71 & 54 & 53.3141 & 33.9963 & 72.632 & 0.4723 & 0.0452 & 0.1891 & 0.3172 \tabularnewline
72 & 65 & 63.6192 & 44.3012 & 82.9371 & 0.4443 & 0.8355 & 0.7157 & 0.7157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202619&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[60])[/C][/ROW]
[ROW][C]48[/C][C]69[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]60[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]56[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]51[/C][C]58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]52[/C][C]50[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]54[/C][C]53[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]55[/C][C]37[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]56[/C][C]22[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]57[/C][C]55[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]58[/C][C]70[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]59[/C][C]62[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]60[/C][C]58[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]39[/C][C]64.0113[/C][C]45.011[/C][C]83.0116[/C][C]0.0049[/C][C]0.7324[/C][C]0.6605[/C][C]0.7324[/C][/ROW]
[ROW][C]62[/C][C]49[/C][C]49.642[/C][C]30.6323[/C][C]68.6517[/C][C]0.4736[/C][C]0.8637[/C][C]0.2561[/C][C]0.1944[/C][/ROW]
[ROW][C]63[/C][C]58[/C][C]59.1884[/C][C]39.979[/C][C]78.3978[/C][C]0.4517[/C][C]0.8507[/C][C]0.5483[/C][C]0.5483[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]52.1368[/C][C]32.8734[/C][C]71.4002[/C][C]0.3006[/C][C]0.2754[/C][C]0.5861[/C][C]0.2754[/C][/ROW]
[ROW][C]65[/C][C]42[/C][C]45.6586[/C][C]26.3655[/C][C]64.9516[/C][C]0.3551[/C][C]0.4458[/C][C]0.2937[/C][C]0.105[/C][/ROW]
[ROW][C]66[/C][C]62[/C][C]58.4371[/C][C]39.1308[/C][C]77.7435[/C][C]0.3588[/C][C]0.9524[/C][C]0.7095[/C][C]0.5177[/C][/ROW]
[ROW][C]67[/C][C]39[/C][C]32.0117[/C][C]12.6991[/C][C]51.3243[/C][C]0.2391[/C][C]0.0012[/C][C]0.3063[/C][C]0.0042[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]24.9814[/C][C]5.6659[/C][C]44.297[/C][C]0.0638[/C][C]0.0774[/C][C]0.6189[/C][C]4e-04[/C][/ROW]
[ROW][C]69[/C][C]72[/C][C]49.9377[/C][C]30.6208[/C][C]69.2546[/C][C]0.0126[/C][C]0.8434[/C][C]0.3037[/C][C]0.2067[/C][/ROW]
[ROW][C]70[/C][C]70[/C][C]63.2904[/C][C]43.9728[/C][C]82.6079[/C][C]0.248[/C][C]0.1884[/C][C]0.248[/C][C]0.7043[/C][/ROW]
[ROW][C]71[/C][C]54[/C][C]53.3141[/C][C]33.9963[/C][C]72.632[/C][C]0.4723[/C][C]0.0452[/C][C]0.1891[/C][C]0.3172[/C][/ROW]
[ROW][C]72[/C][C]65[/C][C]63.6192[/C][C]44.3012[/C][C]82.9371[/C][C]0.4443[/C][C]0.8355[/C][C]0.7157[/C][C]0.7157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202619&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202619&T=1

As an alternative you can also use a 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[60])
4869-------
4960-------
5056-------
5158-------
5250-------
5351-------
5453-------
5537-------
5622-------
5755-------
5870-------
5962-------
6058-------
613964.011345.01183.01160.00490.73240.66050.7324
624949.64230.632368.65170.47360.86370.25610.1944
635859.188439.97978.39780.45170.85070.54830.5483
644752.136832.873471.40020.30060.27540.58610.2754
654245.658626.365564.95160.35510.44580.29370.105
666258.437139.130877.74350.35880.95240.70950.5177
673932.011712.699151.32430.23910.00120.30630.0042
684024.98145.665944.2970.06380.07740.61894e-04
697249.937730.620869.25460.01260.84340.30370.2067
707063.290443.972882.60790.2480.18840.2480.7043
715453.314133.996372.6320.47230.04520.18910.3172
726563.619244.301282.93710.44430.83550.71570.7157







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
610.1514-0.39070625.565600
620.1954-0.01290.20180.4121312.988917.6915
630.1656-0.02010.14121.4124209.1314.4613
640.1885-0.09850.130626.3865163.444112.7845
650.2156-0.08010.120513.3851133.432311.5513
660.16860.0610.110612.694113.309310.6447
670.30780.21830.12648.8367104.098910.2029
680.39450.60120.1854225.5573119.281210.9216
690.19740.44180.2139486.7451160.110512.6535
700.15570.1060.203145.0191148.601412.1902
710.18490.01290.18580.4704135.134911.6248
720.15490.02170.17211.9067124.032611.137

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
61 & 0.1514 & -0.3907 & 0 & 625.5656 & 0 & 0 \tabularnewline
62 & 0.1954 & -0.0129 & 0.2018 & 0.4121 & 312.9889 & 17.6915 \tabularnewline
63 & 0.1656 & -0.0201 & 0.1412 & 1.4124 & 209.13 & 14.4613 \tabularnewline
64 & 0.1885 & -0.0985 & 0.1306 & 26.3865 & 163.4441 & 12.7845 \tabularnewline
65 & 0.2156 & -0.0801 & 0.1205 & 13.3851 & 133.4323 & 11.5513 \tabularnewline
66 & 0.1686 & 0.061 & 0.1106 & 12.694 & 113.3093 & 10.6447 \tabularnewline
67 & 0.3078 & 0.2183 & 0.126 & 48.8367 & 104.0989 & 10.2029 \tabularnewline
68 & 0.3945 & 0.6012 & 0.1854 & 225.5573 & 119.2812 & 10.9216 \tabularnewline
69 & 0.1974 & 0.4418 & 0.2139 & 486.7451 & 160.1105 & 12.6535 \tabularnewline
70 & 0.1557 & 0.106 & 0.2031 & 45.0191 & 148.6014 & 12.1902 \tabularnewline
71 & 0.1849 & 0.0129 & 0.1858 & 0.4704 & 135.1349 & 11.6248 \tabularnewline
72 & 0.1549 & 0.0217 & 0.1721 & 1.9067 & 124.0326 & 11.137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202619&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]61[/C][C]0.1514[/C][C]-0.3907[/C][C]0[/C][C]625.5656[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]62[/C][C]0.1954[/C][C]-0.0129[/C][C]0.2018[/C][C]0.4121[/C][C]312.9889[/C][C]17.6915[/C][/ROW]
[ROW][C]63[/C][C]0.1656[/C][C]-0.0201[/C][C]0.1412[/C][C]1.4124[/C][C]209.13[/C][C]14.4613[/C][/ROW]
[ROW][C]64[/C][C]0.1885[/C][C]-0.0985[/C][C]0.1306[/C][C]26.3865[/C][C]163.4441[/C][C]12.7845[/C][/ROW]
[ROW][C]65[/C][C]0.2156[/C][C]-0.0801[/C][C]0.1205[/C][C]13.3851[/C][C]133.4323[/C][C]11.5513[/C][/ROW]
[ROW][C]66[/C][C]0.1686[/C][C]0.061[/C][C]0.1106[/C][C]12.694[/C][C]113.3093[/C][C]10.6447[/C][/ROW]
[ROW][C]67[/C][C]0.3078[/C][C]0.2183[/C][C]0.126[/C][C]48.8367[/C][C]104.0989[/C][C]10.2029[/C][/ROW]
[ROW][C]68[/C][C]0.3945[/C][C]0.6012[/C][C]0.1854[/C][C]225.5573[/C][C]119.2812[/C][C]10.9216[/C][/ROW]
[ROW][C]69[/C][C]0.1974[/C][C]0.4418[/C][C]0.2139[/C][C]486.7451[/C][C]160.1105[/C][C]12.6535[/C][/ROW]
[ROW][C]70[/C][C]0.1557[/C][C]0.106[/C][C]0.2031[/C][C]45.0191[/C][C]148.6014[/C][C]12.1902[/C][/ROW]
[ROW][C]71[/C][C]0.1849[/C][C]0.0129[/C][C]0.1858[/C][C]0.4704[/C][C]135.1349[/C][C]11.6248[/C][/ROW]
[ROW][C]72[/C][C]0.1549[/C][C]0.0217[/C][C]0.1721[/C][C]1.9067[/C][C]124.0326[/C][C]11.137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202619&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202619&T=2

As an alternative you can also use a QR Code:  

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

Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
610.1514-0.39070625.565600
620.1954-0.01290.20180.4121312.988917.6915
630.1656-0.02010.14121.4124209.1314.4613
640.1885-0.09850.130626.3865163.444112.7845
650.2156-0.08010.120513.3851133.432311.5513
660.16860.0610.110612.694113.309310.6447
670.30780.21830.12648.8367104.098910.2029
680.39450.60120.1854225.5573119.281210.9216
690.19740.44180.2139486.7451160.110512.6535
700.15570.1060.203145.0191148.601412.1902
710.18490.01290.18580.4704135.134911.6248
720.15490.02170.17211.9067124.032611.137



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 1 ; 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')