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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 07:58:18 -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/t1260457383snu18ii9fdxzkfn.htm/, Retrieved Thu, 28 Mar 2024 23:12:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65449, Retrieved Thu, 28 Mar 2024 23:12:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact172
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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Backward Selection] [Workshop 9: ARIMA...] [2009-12-02 17:37:24] [b00a5c3d5f6ccb867aa9e2de58adfa61]
- RMP         [ARIMA Forecasting] [WS 10: Forecasting] [2009-12-10 14:58:18] [63d6214c2814604a6f6cfa44dba5912e] [Current]
-   P           [ARIMA Forecasting] [WS 10: Forecast t...] [2009-12-10 15:18:07] [b00a5c3d5f6ccb867aa9e2de58adfa61]
-    D          [ARIMA Forecasting] [W10] [2009-12-28 11:58:37] [0a7d38ad9c7f1a2c46637c75a8a0e083]
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Dataseries X:
8.1
7.7
7.5
7.6
7.8
7.8
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.2
7.9
7.3
6.9
6.6
6.7
6.9
7.0
7.1
7.2
7.1
6.9
7.0
6.8
6.4
6.7
6.6
6.4
6.3
6.2
6.5
6.8
6.8
6.4
6.1
5.8
6.1
7.2
7.3
6.9
6.1
5.8
6.2
7.1
7.7
7.9
7.7
7.4
7.5
8.0
8.1




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=65449&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=65449&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65449&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 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[48])
366.6-------
376.4-------
386.3-------
396.2-------
406.5-------
416.8-------
426.8-------
436.4-------
446.1-------
455.8-------
466.1-------
477.2-------
487.3-------
496.96.95246.53297.37180.40340.05210.99510.0521
506.16.58265.8947.27130.08480.18320.78940.0206
515.86.4085.53387.28210.08640.75510.67950.0227
526.26.69675.7447.64930.15340.96750.65710.1073
537.17.02796.03398.02190.44350.94870.67340.2958
547.77.16826.13688.19970.15610.55160.75790.4012
557.96.9545.86778.04020.04390.08910.84120.2662
567.76.66885.51337.82430.04010.01840.83270.1421
577.46.42215.19757.64660.05880.02040.84030.08
587.56.71855.43648.00060.11610.14870.82780.187
5987.21965.89088.54840.12480.33960.51150.4528
608.17.27555.9048.6470.11930.15020.4860.486

\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[48]) \tabularnewline
36 & 6.6 & - & - & - & - & - & - & - \tabularnewline
37 & 6.4 & - & - & - & - & - & - & - \tabularnewline
38 & 6.3 & - & - & - & - & - & - & - \tabularnewline
39 & 6.2 & - & - & - & - & - & - & - \tabularnewline
40 & 6.5 & - & - & - & - & - & - & - \tabularnewline
41 & 6.8 & - & - & - & - & - & - & - \tabularnewline
42 & 6.8 & - & - & - & - & - & - & - \tabularnewline
43 & 6.4 & - & - & - & - & - & - & - \tabularnewline
44 & 6.1 & - & - & - & - & - & - & - \tabularnewline
45 & 5.8 & - & - & - & - & - & - & - \tabularnewline
46 & 6.1 & - & - & - & - & - & - & - \tabularnewline
47 & 7.2 & - & - & - & - & - & - & - \tabularnewline
48 & 7.3 & - & - & - & - & - & - & - \tabularnewline
49 & 6.9 & 6.9524 & 6.5329 & 7.3718 & 0.4034 & 0.0521 & 0.9951 & 0.0521 \tabularnewline
50 & 6.1 & 6.5826 & 5.894 & 7.2713 & 0.0848 & 0.1832 & 0.7894 & 0.0206 \tabularnewline
51 & 5.8 & 6.408 & 5.5338 & 7.2821 & 0.0864 & 0.7551 & 0.6795 & 0.0227 \tabularnewline
52 & 6.2 & 6.6967 & 5.744 & 7.6493 & 0.1534 & 0.9675 & 0.6571 & 0.1073 \tabularnewline
53 & 7.1 & 7.0279 & 6.0339 & 8.0219 & 0.4435 & 0.9487 & 0.6734 & 0.2958 \tabularnewline
54 & 7.7 & 7.1682 & 6.1368 & 8.1997 & 0.1561 & 0.5516 & 0.7579 & 0.4012 \tabularnewline
55 & 7.9 & 6.954 & 5.8677 & 8.0402 & 0.0439 & 0.0891 & 0.8412 & 0.2662 \tabularnewline
56 & 7.7 & 6.6688 & 5.5133 & 7.8243 & 0.0401 & 0.0184 & 0.8327 & 0.1421 \tabularnewline
57 & 7.4 & 6.4221 & 5.1975 & 7.6466 & 0.0588 & 0.0204 & 0.8403 & 0.08 \tabularnewline
58 & 7.5 & 6.7185 & 5.4364 & 8.0006 & 0.1161 & 0.1487 & 0.8278 & 0.187 \tabularnewline
59 & 8 & 7.2196 & 5.8908 & 8.5484 & 0.1248 & 0.3396 & 0.5115 & 0.4528 \tabularnewline
60 & 8.1 & 7.2755 & 5.904 & 8.647 & 0.1193 & 0.1502 & 0.486 & 0.486 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65449&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[48])[/C][/ROW]
[ROW][C]36[/C][C]6.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]6.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]6.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]6.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]6.5[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]6.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]6.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]6.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]6.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]5.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]6.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]7.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]7.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]6.9[/C][C]6.9524[/C][C]6.5329[/C][C]7.3718[/C][C]0.4034[/C][C]0.0521[/C][C]0.9951[/C][C]0.0521[/C][/ROW]
[ROW][C]50[/C][C]6.1[/C][C]6.5826[/C][C]5.894[/C][C]7.2713[/C][C]0.0848[/C][C]0.1832[/C][C]0.7894[/C][C]0.0206[/C][/ROW]
[ROW][C]51[/C][C]5.8[/C][C]6.408[/C][C]5.5338[/C][C]7.2821[/C][C]0.0864[/C][C]0.7551[/C][C]0.6795[/C][C]0.0227[/C][/ROW]
[ROW][C]52[/C][C]6.2[/C][C]6.6967[/C][C]5.744[/C][C]7.6493[/C][C]0.1534[/C][C]0.9675[/C][C]0.6571[/C][C]0.1073[/C][/ROW]
[ROW][C]53[/C][C]7.1[/C][C]7.0279[/C][C]6.0339[/C][C]8.0219[/C][C]0.4435[/C][C]0.9487[/C][C]0.6734[/C][C]0.2958[/C][/ROW]
[ROW][C]54[/C][C]7.7[/C][C]7.1682[/C][C]6.1368[/C][C]8.1997[/C][C]0.1561[/C][C]0.5516[/C][C]0.7579[/C][C]0.4012[/C][/ROW]
[ROW][C]55[/C][C]7.9[/C][C]6.954[/C][C]5.8677[/C][C]8.0402[/C][C]0.0439[/C][C]0.0891[/C][C]0.8412[/C][C]0.2662[/C][/ROW]
[ROW][C]56[/C][C]7.7[/C][C]6.6688[/C][C]5.5133[/C][C]7.8243[/C][C]0.0401[/C][C]0.0184[/C][C]0.8327[/C][C]0.1421[/C][/ROW]
[ROW][C]57[/C][C]7.4[/C][C]6.4221[/C][C]5.1975[/C][C]7.6466[/C][C]0.0588[/C][C]0.0204[/C][C]0.8403[/C][C]0.08[/C][/ROW]
[ROW][C]58[/C][C]7.5[/C][C]6.7185[/C][C]5.4364[/C][C]8.0006[/C][C]0.1161[/C][C]0.1487[/C][C]0.8278[/C][C]0.187[/C][/ROW]
[ROW][C]59[/C][C]8[/C][C]7.2196[/C][C]5.8908[/C][C]8.5484[/C][C]0.1248[/C][C]0.3396[/C][C]0.5115[/C][C]0.4528[/C][/ROW]
[ROW][C]60[/C][C]8.1[/C][C]7.2755[/C][C]5.904[/C][C]8.647[/C][C]0.1193[/C][C]0.1502[/C][C]0.486[/C][C]0.486[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65449&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65449&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[48])
366.6-------
376.4-------
386.3-------
396.2-------
406.5-------
416.8-------
426.8-------
436.4-------
446.1-------
455.8-------
466.1-------
477.2-------
487.3-------
496.96.95246.53297.37180.40340.05210.99510.0521
506.16.58265.8947.27130.08480.18320.78940.0206
515.86.4085.53387.28210.08640.75510.67950.0227
526.26.69675.7447.64930.15340.96750.65710.1073
537.17.02796.03398.02190.44350.94870.67340.2958
547.77.16826.13688.19970.15610.55160.75790.4012
557.96.9545.86778.04020.04390.08910.84120.2662
567.76.66885.51337.82430.04010.01840.83270.1421
577.46.42215.19757.64660.05880.02040.84030.08
587.56.71855.43648.00060.11610.14870.82780.187
5987.21965.89088.54840.12480.33960.51150.4528
608.17.27555.9048.6470.11930.15020.4860.486







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
490.0308-0.007500.002700
500.0534-0.07330.04040.23290.11780.3433
510.0696-0.09490.05860.36960.20180.4492
520.0726-0.07420.06250.24670.2130.4615
530.07220.01030.0520.00520.17140.4141
540.07340.07420.05570.28280.190.4359
550.07970.1360.06720.8950.29070.5392
560.08840.15460.07811.06340.38730.6223
570.09730.15230.08640.95640.45050.6712
580.09740.11630.08940.61080.46650.683
590.09390.10810.09110.60910.47950.6925
600.09620.11330.09290.67990.49620.7044

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
49 & 0.0308 & -0.0075 & 0 & 0.0027 & 0 & 0 \tabularnewline
50 & 0.0534 & -0.0733 & 0.0404 & 0.2329 & 0.1178 & 0.3433 \tabularnewline
51 & 0.0696 & -0.0949 & 0.0586 & 0.3696 & 0.2018 & 0.4492 \tabularnewline
52 & 0.0726 & -0.0742 & 0.0625 & 0.2467 & 0.213 & 0.4615 \tabularnewline
53 & 0.0722 & 0.0103 & 0.052 & 0.0052 & 0.1714 & 0.4141 \tabularnewline
54 & 0.0734 & 0.0742 & 0.0557 & 0.2828 & 0.19 & 0.4359 \tabularnewline
55 & 0.0797 & 0.136 & 0.0672 & 0.895 & 0.2907 & 0.5392 \tabularnewline
56 & 0.0884 & 0.1546 & 0.0781 & 1.0634 & 0.3873 & 0.6223 \tabularnewline
57 & 0.0973 & 0.1523 & 0.0864 & 0.9564 & 0.4505 & 0.6712 \tabularnewline
58 & 0.0974 & 0.1163 & 0.0894 & 0.6108 & 0.4665 & 0.683 \tabularnewline
59 & 0.0939 & 0.1081 & 0.0911 & 0.6091 & 0.4795 & 0.6925 \tabularnewline
60 & 0.0962 & 0.1133 & 0.0929 & 0.6799 & 0.4962 & 0.7044 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65449&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]49[/C][C]0.0308[/C][C]-0.0075[/C][C]0[/C][C]0.0027[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]50[/C][C]0.0534[/C][C]-0.0733[/C][C]0.0404[/C][C]0.2329[/C][C]0.1178[/C][C]0.3433[/C][/ROW]
[ROW][C]51[/C][C]0.0696[/C][C]-0.0949[/C][C]0.0586[/C][C]0.3696[/C][C]0.2018[/C][C]0.4492[/C][/ROW]
[ROW][C]52[/C][C]0.0726[/C][C]-0.0742[/C][C]0.0625[/C][C]0.2467[/C][C]0.213[/C][C]0.4615[/C][/ROW]
[ROW][C]53[/C][C]0.0722[/C][C]0.0103[/C][C]0.052[/C][C]0.0052[/C][C]0.1714[/C][C]0.4141[/C][/ROW]
[ROW][C]54[/C][C]0.0734[/C][C]0.0742[/C][C]0.0557[/C][C]0.2828[/C][C]0.19[/C][C]0.4359[/C][/ROW]
[ROW][C]55[/C][C]0.0797[/C][C]0.136[/C][C]0.0672[/C][C]0.895[/C][C]0.2907[/C][C]0.5392[/C][/ROW]
[ROW][C]56[/C][C]0.0884[/C][C]0.1546[/C][C]0.0781[/C][C]1.0634[/C][C]0.3873[/C][C]0.6223[/C][/ROW]
[ROW][C]57[/C][C]0.0973[/C][C]0.1523[/C][C]0.0864[/C][C]0.9564[/C][C]0.4505[/C][C]0.6712[/C][/ROW]
[ROW][C]58[/C][C]0.0974[/C][C]0.1163[/C][C]0.0894[/C][C]0.6108[/C][C]0.4665[/C][C]0.683[/C][/ROW]
[ROW][C]59[/C][C]0.0939[/C][C]0.1081[/C][C]0.0911[/C][C]0.6091[/C][C]0.4795[/C][C]0.6925[/C][/ROW]
[ROW][C]60[/C][C]0.0962[/C][C]0.1133[/C][C]0.0929[/C][C]0.6799[/C][C]0.4962[/C][C]0.7044[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65449&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65449&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
490.0308-0.007500.002700
500.0534-0.07330.04040.23290.11780.3433
510.0696-0.09490.05860.36960.20180.4492
520.0726-0.07420.06250.24670.2130.4615
530.07220.01030.0520.00520.17140.4141
540.07340.07420.05570.28280.190.4359
550.07970.1360.06720.8950.29070.5392
560.08840.15460.07811.06340.38730.6223
570.09730.15230.08640.95640.45050.6712
580.09740.11630.08940.61080.46650.683
590.09390.10810.09110.60910.47950.6925
600.09620.11330.09290.67990.49620.7044



Parameters (Session):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 1 ; 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')