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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, 28 Dec 2010 14:20:00 +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/28/t12935458814izxf0gsid70eb3.htm/, Retrieved Sat, 04 May 2024 23:19:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116376, Retrieved Sat, 04 May 2024 23:19:00 +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 Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
F   PD      [ARIMA Backward Selection] [Workshop 6 'Aanta...] [2010-12-14 18:47:02] [40c8b935cbad1b0be3c22a481f9723f7]
- R P         [ARIMA Backward Selection] [ARIMA backward se...] [2010-12-17 15:45:37] [75b8170d590d2aca2c97c1862bb2167f]
-   PD          [ARIMA Backward Selection] [ARIMA BACKWARD Se...] [2010-12-26 13:20:22] [c895532cb7349383dee5125244983cc8]
- RMP             [ARIMA Forecasting] [] [2010-12-26 14:45:55] [c895532cb7349383dee5125244983cc8]
-                   [ARIMA Forecasting] [TUMBLER] [2010-12-26 14:49:10] [c895532cb7349383dee5125244983cc8]
-   P                   [ARIMA Forecasting] [berekening 16] [2010-12-28 14:20:00] [87bb5e10c18d96bd329dff2d857096c8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10
10
10
10
10
9.94
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
10.06
9.94
9.94
9.94
9.94
9.94
9.94
10.06
10.06
9.94
10.06
10.06
10.06
10.18
10.28
10.28
10.18
10.28
10.28
10.28
10.18
10.28
10.28
10.18
10.18
10.18
10.28
10.28
10.18
10.18
10.18
10.18
10.18
10.18
10.28
10.28
10.28
10.18
10.18
10.18
10.28
10.18
10.18
10.28
10.18
10.18
10.18
10.28
10.28
10.28
10.28
10.28
10.28
10.18
10.18
10.18
10.18
10.18
10.18
10.18
10.18
10.18
10.28
10.28
10.28
10.28
10.28
10.28
10.28
10.28
10.18
10.28
10.28
10.28
10.28
10.18
10.28
10.28
10.28
10.18
10.18
10.28
10.28
10.28
10.28
10.28
10.28
10.28
10.18
10.28
10.28
10.28
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.34
10.42
10.42
10.42
10.42
10.34
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.34
10.34
10.42
10.42
10.34
10.34
10.34
10.42
10.42
10.42
10.34
10.34
10.34
10.34
10.34
10.42
10.42
10.42
10.34
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.34
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.42
10.34
10.34
10.42
10.42
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.49
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.49
10.55
10.55
10.55
10.49
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.55
10.49
10.49
10.49
10.55
10.55
10.55
10.49
10.55
10.55
10.55
10.63
10.63
10.63
10.63
10.63
10.57
10.63
10.63
10.63
10.63
10.57
10.57
10.57
10.63
10.63
10.63
10.63
10.57
10.63
10.63
10.57
10.63
10.63
10.63
10.63
10.57
10.63
10.6
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.6
10.6
10.6
10.6
10.7
10.7
10.6
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.7
10.8
10.8
10.8
10.8
10.7
10.7
10.7
10.85
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.75
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.85
10.75
10.75
10.85
10.85
10.85
10.85
10.75
10.75
10.75
10.75
10.75

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=116376&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=116376&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116376&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[338])
33710.85-------
33810.85-------
33910.8510.846710.763410.93010.46940.46940.46940.4694
34010.7510.845210.743310.94720.03350.46340.46340.4634
34110.7510.844510.732210.95690.04960.95040.95040.4619
34210.8510.844210.724310.96410.46220.93820.93820.4622
34310.8510.84410.717810.97030.46320.46320.46320.4632
34410.8510.84410.71210.97590.46440.46440.46440.4644
34510.8510.843910.706710.98120.46560.46560.46560.4656
34610.7510.843910.701610.98620.09790.46670.46670.4667
34710.7510.843910.696810.99110.10550.89450.89450.4677
34810.7510.843910.692110.99580.11270.88730.88730.4687
34910.7510.843910.687511.00030.11960.88040.88040.4696
35010.7510.843910.683111.00470.12610.87390.87390.4704

\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[338]) \tabularnewline
337 & 10.85 & - & - & - & - & - & - & - \tabularnewline
338 & 10.85 & - & - & - & - & - & - & - \tabularnewline
339 & 10.85 & 10.8467 & 10.7634 & 10.9301 & 0.4694 & 0.4694 & 0.4694 & 0.4694 \tabularnewline
340 & 10.75 & 10.8452 & 10.7433 & 10.9472 & 0.0335 & 0.4634 & 0.4634 & 0.4634 \tabularnewline
341 & 10.75 & 10.8445 & 10.7322 & 10.9569 & 0.0496 & 0.9504 & 0.9504 & 0.4619 \tabularnewline
342 & 10.85 & 10.8442 & 10.7243 & 10.9641 & 0.4622 & 0.9382 & 0.9382 & 0.4622 \tabularnewline
343 & 10.85 & 10.844 & 10.7178 & 10.9703 & 0.4632 & 0.4632 & 0.4632 & 0.4632 \tabularnewline
344 & 10.85 & 10.844 & 10.712 & 10.9759 & 0.4644 & 0.4644 & 0.4644 & 0.4644 \tabularnewline
345 & 10.85 & 10.8439 & 10.7067 & 10.9812 & 0.4656 & 0.4656 & 0.4656 & 0.4656 \tabularnewline
346 & 10.75 & 10.8439 & 10.7016 & 10.9862 & 0.0979 & 0.4667 & 0.4667 & 0.4667 \tabularnewline
347 & 10.75 & 10.8439 & 10.6968 & 10.9911 & 0.1055 & 0.8945 & 0.8945 & 0.4677 \tabularnewline
348 & 10.75 & 10.8439 & 10.6921 & 10.9958 & 0.1127 & 0.8873 & 0.8873 & 0.4687 \tabularnewline
349 & 10.75 & 10.8439 & 10.6875 & 11.0003 & 0.1196 & 0.8804 & 0.8804 & 0.4696 \tabularnewline
350 & 10.75 & 10.8439 & 10.6831 & 11.0047 & 0.1261 & 0.8739 & 0.8739 & 0.4704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116376&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[338])[/C][/ROW]
[ROW][C]337[/C][C]10.85[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]338[/C][C]10.85[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]339[/C][C]10.85[/C][C]10.8467[/C][C]10.7634[/C][C]10.9301[/C][C]0.4694[/C][C]0.4694[/C][C]0.4694[/C][C]0.4694[/C][/ROW]
[ROW][C]340[/C][C]10.75[/C][C]10.8452[/C][C]10.7433[/C][C]10.9472[/C][C]0.0335[/C][C]0.4634[/C][C]0.4634[/C][C]0.4634[/C][/ROW]
[ROW][C]341[/C][C]10.75[/C][C]10.8445[/C][C]10.7322[/C][C]10.9569[/C][C]0.0496[/C][C]0.9504[/C][C]0.9504[/C][C]0.4619[/C][/ROW]
[ROW][C]342[/C][C]10.85[/C][C]10.8442[/C][C]10.7243[/C][C]10.9641[/C][C]0.4622[/C][C]0.9382[/C][C]0.9382[/C][C]0.4622[/C][/ROW]
[ROW][C]343[/C][C]10.85[/C][C]10.844[/C][C]10.7178[/C][C]10.9703[/C][C]0.4632[/C][C]0.4632[/C][C]0.4632[/C][C]0.4632[/C][/ROW]
[ROW][C]344[/C][C]10.85[/C][C]10.844[/C][C]10.712[/C][C]10.9759[/C][C]0.4644[/C][C]0.4644[/C][C]0.4644[/C][C]0.4644[/C][/ROW]
[ROW][C]345[/C][C]10.85[/C][C]10.8439[/C][C]10.7067[/C][C]10.9812[/C][C]0.4656[/C][C]0.4656[/C][C]0.4656[/C][C]0.4656[/C][/ROW]
[ROW][C]346[/C][C]10.75[/C][C]10.8439[/C][C]10.7016[/C][C]10.9862[/C][C]0.0979[/C][C]0.4667[/C][C]0.4667[/C][C]0.4667[/C][/ROW]
[ROW][C]347[/C][C]10.75[/C][C]10.8439[/C][C]10.6968[/C][C]10.9911[/C][C]0.1055[/C][C]0.8945[/C][C]0.8945[/C][C]0.4677[/C][/ROW]
[ROW][C]348[/C][C]10.75[/C][C]10.8439[/C][C]10.6921[/C][C]10.9958[/C][C]0.1127[/C][C]0.8873[/C][C]0.8873[/C][C]0.4687[/C][/ROW]
[ROW][C]349[/C][C]10.75[/C][C]10.8439[/C][C]10.6875[/C][C]11.0003[/C][C]0.1196[/C][C]0.8804[/C][C]0.8804[/C][C]0.4696[/C][/ROW]
[ROW][C]350[/C][C]10.75[/C][C]10.8439[/C][C]10.6831[/C][C]11.0047[/C][C]0.1261[/C][C]0.8739[/C][C]0.8739[/C][C]0.4704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116376&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116376&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[338])
33710.85-------
33810.85-------
33910.8510.846710.763410.93010.46940.46940.46940.4694
34010.7510.845210.743310.94720.03350.46340.46340.4634
34110.7510.844510.732210.95690.04960.95040.95040.4619
34210.8510.844210.724310.96410.46220.93820.93820.4622
34310.8510.84410.717810.97030.46320.46320.46320.4632
34410.8510.84410.71210.97590.46440.46440.46440.4644
34510.8510.843910.706710.98120.46560.46560.46560.4656
34610.7510.843910.701610.98620.09790.46670.46670.4667
34710.7510.843910.696810.99110.10550.89450.89450.4677
34810.7510.843910.692110.99580.11270.88730.88730.4687
34910.7510.843910.687511.00030.11960.88040.88040.4696
35010.7510.843910.683111.00470.12610.87390.87390.4704






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
3390.00393e-040000
3400.0048-0.00880.00450.00910.00450.0674
3410.0053-0.00870.00590.00890.0060.0775
3420.00565e-040.004600.00450.0672
3430.00595e-040.003800.00360.0601
3440.00626e-040.003200.0030.055
3450.00656e-040.002900.00260.0509
3460.0067-0.00870.00360.00880.00340.0581
3470.0069-0.00870.00410.00880.0040.0631
3480.0071-0.00870.00460.00880.00450.0668
3490.0074-0.00870.0050.00880.00490.0697
3500.0076-0.00870.00530.00880.00520.072

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
339 & 0.0039 & 3e-04 & 0 & 0 & 0 & 0 \tabularnewline
340 & 0.0048 & -0.0088 & 0.0045 & 0.0091 & 0.0045 & 0.0674 \tabularnewline
341 & 0.0053 & -0.0087 & 0.0059 & 0.0089 & 0.006 & 0.0775 \tabularnewline
342 & 0.0056 & 5e-04 & 0.0046 & 0 & 0.0045 & 0.0672 \tabularnewline
343 & 0.0059 & 5e-04 & 0.0038 & 0 & 0.0036 & 0.0601 \tabularnewline
344 & 0.0062 & 6e-04 & 0.0032 & 0 & 0.003 & 0.055 \tabularnewline
345 & 0.0065 & 6e-04 & 0.0029 & 0 & 0.0026 & 0.0509 \tabularnewline
346 & 0.0067 & -0.0087 & 0.0036 & 0.0088 & 0.0034 & 0.0581 \tabularnewline
347 & 0.0069 & -0.0087 & 0.0041 & 0.0088 & 0.004 & 0.0631 \tabularnewline
348 & 0.0071 & -0.0087 & 0.0046 & 0.0088 & 0.0045 & 0.0668 \tabularnewline
349 & 0.0074 & -0.0087 & 0.005 & 0.0088 & 0.0049 & 0.0697 \tabularnewline
350 & 0.0076 & -0.0087 & 0.0053 & 0.0088 & 0.0052 & 0.072 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116376&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]339[/C][C]0.0039[/C][C]3e-04[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]340[/C][C]0.0048[/C][C]-0.0088[/C][C]0.0045[/C][C]0.0091[/C][C]0.0045[/C][C]0.0674[/C][/ROW]
[ROW][C]341[/C][C]0.0053[/C][C]-0.0087[/C][C]0.0059[/C][C]0.0089[/C][C]0.006[/C][C]0.0775[/C][/ROW]
[ROW][C]342[/C][C]0.0056[/C][C]5e-04[/C][C]0.0046[/C][C]0[/C][C]0.0045[/C][C]0.0672[/C][/ROW]
[ROW][C]343[/C][C]0.0059[/C][C]5e-04[/C][C]0.0038[/C][C]0[/C][C]0.0036[/C][C]0.0601[/C][/ROW]
[ROW][C]344[/C][C]0.0062[/C][C]6e-04[/C][C]0.0032[/C][C]0[/C][C]0.003[/C][C]0.055[/C][/ROW]
[ROW][C]345[/C][C]0.0065[/C][C]6e-04[/C][C]0.0029[/C][C]0[/C][C]0.0026[/C][C]0.0509[/C][/ROW]
[ROW][C]346[/C][C]0.0067[/C][C]-0.0087[/C][C]0.0036[/C][C]0.0088[/C][C]0.0034[/C][C]0.0581[/C][/ROW]
[ROW][C]347[/C][C]0.0069[/C][C]-0.0087[/C][C]0.0041[/C][C]0.0088[/C][C]0.004[/C][C]0.0631[/C][/ROW]
[ROW][C]348[/C][C]0.0071[/C][C]-0.0087[/C][C]0.0046[/C][C]0.0088[/C][C]0.0045[/C][C]0.0668[/C][/ROW]
[ROW][C]349[/C][C]0.0074[/C][C]-0.0087[/C][C]0.005[/C][C]0.0088[/C][C]0.0049[/C][C]0.0697[/C][/ROW]
[ROW][C]350[/C][C]0.0076[/C][C]-0.0087[/C][C]0.0053[/C][C]0.0088[/C][C]0.0052[/C][C]0.072[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116376&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116376&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
3390.00393e-040000
3400.0048-0.00880.00450.00910.00450.0674
3410.0053-0.00870.00590.00890.0060.0775
3420.00565e-040.004600.00450.0672
3430.00595e-040.003800.00360.0601
3440.00626e-040.003200.0030.055
3450.00656e-040.002900.00260.0509
3460.0067-0.00870.00360.00880.00340.0581
3470.0069-0.00870.00410.00880.0040.0631
3480.0071-0.00870.00460.00880.00450.0668
3490.0074-0.00870.0050.00880.00490.0697
3500.0076-0.00870.00530.00880.00520.072


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = 12 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 1 ; par6 = 1 ; par7 = 1 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')