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Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationThu, 10 Dec 2009 03:44:34 -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/10/t1260441932yrrxtlldxnsre2n.htm/, Retrieved Fri, 29 Mar 2024 13:43:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65259, Retrieved Fri, 29 Mar 2024 13:43:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact173
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Forecasting] [ARIMA forecasting...] [2009-12-10 10:44:34] [a5b01ef1969ffd97a40c5fefe56a50d0] [Current]
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Dataseries X:
1.8
1.6
1.9
1.7
1.6
1.3
1.1
1.9
2.6
2.3
2.4
2.2
2
2.9
2.6
2.3
2.3
2.6
3.1
2.8
2.5
2.9
3.1
3.1
3.2
2.5
2.6
2.9
2.6
2.4
1.7
2
2.2
1.9
1.6
1.6
1.2
1.2
1.5
1.6
1.7
1.8
1.8
1.8
1.3
1.3
1.4
1.1
1.5
2.2
2.9
3.1
3.5
3.6
4.4
4.2
5.2
5.8
5.9
5.4
5.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65259&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65259&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65259&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 time2 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])
371.2-------
381.2-------
391.5-------
401.6-------
411.7-------
421.8-------
431.8-------
441.8-------
451.3-------
461.3-------
471.4-------
481.1-------
491.5-------
502.21.38291.0191.98310.00380.35110.72480.3511
512.91.42610.93222.44880.00240.0690.44370.4437
523.11.49750.88983.03230.02040.03660.44790.4987
533.51.42040.79783.20530.01120.03260.37940.4652
543.61.38750.73993.48910.01950.02440.35020.4582
554.41.15760.61712.91321e-040.00320.23660.3511
564.21.26120.62843.70720.00930.00590.3330.4241
575.21.27660.60874.19260.00420.02470.49370.4403
585.81.1940.56034.09119e-040.00340.47140.418
595.91.09280.51013.79872e-043e-040.4120.384
605.41.06890.48743.96670.00175e-040.49160.3853
615.50.92090.42843.23211e-041e-040.31170.3117

\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 & 1.2 & - & - & - & - & - & - & - \tabularnewline
38 & 1.2 & - & - & - & - & - & - & - \tabularnewline
39 & 1.5 & - & - & - & - & - & - & - \tabularnewline
40 & 1.6 & - & - & - & - & - & - & - \tabularnewline
41 & 1.7 & - & - & - & - & - & - & - \tabularnewline
42 & 1.8 & - & - & - & - & - & - & - \tabularnewline
43 & 1.8 & - & - & - & - & - & - & - \tabularnewline
44 & 1.8 & - & - & - & - & - & - & - \tabularnewline
45 & 1.3 & - & - & - & - & - & - & - \tabularnewline
46 & 1.3 & - & - & - & - & - & - & - \tabularnewline
47 & 1.4 & - & - & - & - & - & - & - \tabularnewline
48 & 1.1 & - & - & - & - & - & - & - \tabularnewline
49 & 1.5 & - & - & - & - & - & - & - \tabularnewline
50 & 2.2 & 1.3829 & 1.019 & 1.9831 & 0.0038 & 0.3511 & 0.7248 & 0.3511 \tabularnewline
51 & 2.9 & 1.4261 & 0.9322 & 2.4488 & 0.0024 & 0.069 & 0.4437 & 0.4437 \tabularnewline
52 & 3.1 & 1.4975 & 0.8898 & 3.0323 & 0.0204 & 0.0366 & 0.4479 & 0.4987 \tabularnewline
53 & 3.5 & 1.4204 & 0.7978 & 3.2053 & 0.0112 & 0.0326 & 0.3794 & 0.4652 \tabularnewline
54 & 3.6 & 1.3875 & 0.7399 & 3.4891 & 0.0195 & 0.0244 & 0.3502 & 0.4582 \tabularnewline
55 & 4.4 & 1.1576 & 0.6171 & 2.9132 & 1e-04 & 0.0032 & 0.2366 & 0.3511 \tabularnewline
56 & 4.2 & 1.2612 & 0.6284 & 3.7072 & 0.0093 & 0.0059 & 0.333 & 0.4241 \tabularnewline
57 & 5.2 & 1.2766 & 0.6087 & 4.1926 & 0.0042 & 0.0247 & 0.4937 & 0.4403 \tabularnewline
58 & 5.8 & 1.194 & 0.5603 & 4.0911 & 9e-04 & 0.0034 & 0.4714 & 0.418 \tabularnewline
59 & 5.9 & 1.0928 & 0.5101 & 3.7987 & 2e-04 & 3e-04 & 0.412 & 0.384 \tabularnewline
60 & 5.4 & 1.0689 & 0.4874 & 3.9667 & 0.0017 & 5e-04 & 0.4916 & 0.3853 \tabularnewline
61 & 5.5 & 0.9209 & 0.4284 & 3.2321 & 1e-04 & 1e-04 & 0.3117 & 0.3117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65259&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]1.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]1.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]1.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]1.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]1.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]1.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]1.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]1.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]1.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]1.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]1.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]1.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]1.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]2.2[/C][C]1.3829[/C][C]1.019[/C][C]1.9831[/C][C]0.0038[/C][C]0.3511[/C][C]0.7248[/C][C]0.3511[/C][/ROW]
[ROW][C]51[/C][C]2.9[/C][C]1.4261[/C][C]0.9322[/C][C]2.4488[/C][C]0.0024[/C][C]0.069[/C][C]0.4437[/C][C]0.4437[/C][/ROW]
[ROW][C]52[/C][C]3.1[/C][C]1.4975[/C][C]0.8898[/C][C]3.0323[/C][C]0.0204[/C][C]0.0366[/C][C]0.4479[/C][C]0.4987[/C][/ROW]
[ROW][C]53[/C][C]3.5[/C][C]1.4204[/C][C]0.7978[/C][C]3.2053[/C][C]0.0112[/C][C]0.0326[/C][C]0.3794[/C][C]0.4652[/C][/ROW]
[ROW][C]54[/C][C]3.6[/C][C]1.3875[/C][C]0.7399[/C][C]3.4891[/C][C]0.0195[/C][C]0.0244[/C][C]0.3502[/C][C]0.4582[/C][/ROW]
[ROW][C]55[/C][C]4.4[/C][C]1.1576[/C][C]0.6171[/C][C]2.9132[/C][C]1e-04[/C][C]0.0032[/C][C]0.2366[/C][C]0.3511[/C][/ROW]
[ROW][C]56[/C][C]4.2[/C][C]1.2612[/C][C]0.6284[/C][C]3.7072[/C][C]0.0093[/C][C]0.0059[/C][C]0.333[/C][C]0.4241[/C][/ROW]
[ROW][C]57[/C][C]5.2[/C][C]1.2766[/C][C]0.6087[/C][C]4.1926[/C][C]0.0042[/C][C]0.0247[/C][C]0.4937[/C][C]0.4403[/C][/ROW]
[ROW][C]58[/C][C]5.8[/C][C]1.194[/C][C]0.5603[/C][C]4.0911[/C][C]9e-04[/C][C]0.0034[/C][C]0.4714[/C][C]0.418[/C][/ROW]
[ROW][C]59[/C][C]5.9[/C][C]1.0928[/C][C]0.5101[/C][C]3.7987[/C][C]2e-04[/C][C]3e-04[/C][C]0.412[/C][C]0.384[/C][/ROW]
[ROW][C]60[/C][C]5.4[/C][C]1.0689[/C][C]0.4874[/C][C]3.9667[/C][C]0.0017[/C][C]5e-04[/C][C]0.4916[/C][C]0.3853[/C][/ROW]
[ROW][C]61[/C][C]5.5[/C][C]0.9209[/C][C]0.4284[/C][C]3.2321[/C][C]1e-04[/C][C]1e-04[/C][C]0.3117[/C][C]0.3117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65259&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65259&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[49])
371.2-------
381.2-------
391.5-------
401.6-------
411.7-------
421.8-------
431.8-------
441.8-------
451.3-------
461.3-------
471.4-------
481.1-------
491.5-------
502.21.38291.0191.98310.00380.35110.72480.3511
512.91.42610.93222.44880.00240.0690.44370.4437
523.11.49750.88983.03230.02040.03660.44790.4987
533.51.42040.79783.20530.01120.03260.37940.4652
543.61.38750.73993.48910.01950.02440.35020.4582
554.41.15760.61712.91321e-040.00320.23660.3511
564.21.26120.62843.70720.00930.00590.3330.4241
575.21.27660.60874.19260.00420.02470.49370.4403
585.81.1940.56034.09119e-040.00340.47140.418
595.91.09280.51013.79872e-043e-040.4120.384
605.41.06890.48743.96670.00175e-040.49160.3853
615.50.92090.42843.23211e-041e-040.31170.3117







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
500.22140.590900.667700
510.36591.03350.81222.17231.421.1916
520.52291.07010.89822.5681.80271.3426
530.64111.4641.03964.32462.43321.5599
540.77281.59471.15064.89532.92561.7104
550.77372.80081.425710.51294.19012.047
560.98952.331.55498.63634.82532.1967
571.16543.07331.744715.39316.14632.4792
581.23793.85751.979421.21497.82062.7965
591.26334.39882.221423.10889.34943.0577
601.38324.05212.387818.758710.20483.1945
611.28054.97252.603220.968211.10173.3319

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
50 & 0.2214 & 0.5909 & 0 & 0.6677 & 0 & 0 \tabularnewline
51 & 0.3659 & 1.0335 & 0.8122 & 2.1723 & 1.42 & 1.1916 \tabularnewline
52 & 0.5229 & 1.0701 & 0.8982 & 2.568 & 1.8027 & 1.3426 \tabularnewline
53 & 0.6411 & 1.464 & 1.0396 & 4.3246 & 2.4332 & 1.5599 \tabularnewline
54 & 0.7728 & 1.5947 & 1.1506 & 4.8953 & 2.9256 & 1.7104 \tabularnewline
55 & 0.7737 & 2.8008 & 1.4257 & 10.5129 & 4.1901 & 2.047 \tabularnewline
56 & 0.9895 & 2.33 & 1.5549 & 8.6363 & 4.8253 & 2.1967 \tabularnewline
57 & 1.1654 & 3.0733 & 1.7447 & 15.3931 & 6.1463 & 2.4792 \tabularnewline
58 & 1.2379 & 3.8575 & 1.9794 & 21.2149 & 7.8206 & 2.7965 \tabularnewline
59 & 1.2633 & 4.3988 & 2.2214 & 23.1088 & 9.3494 & 3.0577 \tabularnewline
60 & 1.3832 & 4.0521 & 2.3878 & 18.7587 & 10.2048 & 3.1945 \tabularnewline
61 & 1.2805 & 4.9725 & 2.6032 & 20.9682 & 11.1017 & 3.3319 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65259&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]0.2214[/C][C]0.5909[/C][C]0[/C][C]0.6677[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]51[/C][C]0.3659[/C][C]1.0335[/C][C]0.8122[/C][C]2.1723[/C][C]1.42[/C][C]1.1916[/C][/ROW]
[ROW][C]52[/C][C]0.5229[/C][C]1.0701[/C][C]0.8982[/C][C]2.568[/C][C]1.8027[/C][C]1.3426[/C][/ROW]
[ROW][C]53[/C][C]0.6411[/C][C]1.464[/C][C]1.0396[/C][C]4.3246[/C][C]2.4332[/C][C]1.5599[/C][/ROW]
[ROW][C]54[/C][C]0.7728[/C][C]1.5947[/C][C]1.1506[/C][C]4.8953[/C][C]2.9256[/C][C]1.7104[/C][/ROW]
[ROW][C]55[/C][C]0.7737[/C][C]2.8008[/C][C]1.4257[/C][C]10.5129[/C][C]4.1901[/C][C]2.047[/C][/ROW]
[ROW][C]56[/C][C]0.9895[/C][C]2.33[/C][C]1.5549[/C][C]8.6363[/C][C]4.8253[/C][C]2.1967[/C][/ROW]
[ROW][C]57[/C][C]1.1654[/C][C]3.0733[/C][C]1.7447[/C][C]15.3931[/C][C]6.1463[/C][C]2.4792[/C][/ROW]
[ROW][C]58[/C][C]1.2379[/C][C]3.8575[/C][C]1.9794[/C][C]21.2149[/C][C]7.8206[/C][C]2.7965[/C][/ROW]
[ROW][C]59[/C][C]1.2633[/C][C]4.3988[/C][C]2.2214[/C][C]23.1088[/C][C]9.3494[/C][C]3.0577[/C][/ROW]
[ROW][C]60[/C][C]1.3832[/C][C]4.0521[/C][C]2.3878[/C][C]18.7587[/C][C]10.2048[/C][C]3.1945[/C][/ROW]
[ROW][C]61[/C][C]1.2805[/C][C]4.9725[/C][C]2.6032[/C][C]20.9682[/C][C]11.1017[/C][C]3.3319[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65259&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65259&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
500.22140.590900.667700
510.36591.03350.81222.17231.421.1916
520.52291.07010.89822.5681.80271.3426
530.64111.4641.03964.32462.43321.5599
540.77281.59471.15064.89532.92561.7104
550.77372.80081.425710.51294.19012.047
560.98952.331.55498.63634.82532.1967
571.16543.07331.744715.39316.14632.4792
581.23793.85751.979421.21497.82062.7965
591.26334.39882.221423.10889.34943.0577
601.38324.05212.387818.758710.20483.1945
611.28054.97252.603220.968211.10173.3319



Parameters (Session):
par1 = 12 ; par2 = -0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 2 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = -0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 0 ; par8 = 2 ; 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')