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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 computationWed, 16 Dec 2009 13:56:07 -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/16/t1260997028g4dt7mvqzsjjp3k.htm/, Retrieved Tue, 30 Apr 2024 13:06:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68601, Retrieved Tue, 30 Apr 2024 13:06:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact124

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variance Reduction Matrix] [VRM - vaste rente...] [2008-12-12 10:51:08] [c5a66f1c8528a963efc2b82a8519f117]
- RM D  [Standard Deviation-Mean Plot] [SDMP inschrijving...] [2008-12-12 11:03:14] [c5a66f1c8528a963efc2b82a8519f117]
- RM      [Variance Reduction Matrix] [VRM - inschrijvin...] [2008-12-12 11:08:27] [c5a66f1c8528a963efc2b82a8519f117]
- RMP       [(Partial) Autocorrelation Function] [ACF - inschrijvin...] [2008-12-12 11:14:36] [c5a66f1c8528a963efc2b82a8519f117]
-   P         [(Partial) Autocorrelation Function] [ACF - inschrijvin...] [2008-12-12 11:32:48] [c5a66f1c8528a963efc2b82a8519f117]
-               [(Partial) Autocorrelation Function] [ACF - inschrijvin...] [2008-12-12 11:37:19] [c5a66f1c8528a963efc2b82a8519f117]
- RM              [ARIMA Backward Selection] [ARIMA backward se...] [2008-12-12 11:51:13] [c5a66f1c8528a963efc2b82a8519f117]
- RM                [ARIMA Forecasting] [ARIMA forecasting...] [2008-12-12 11:57:42] [c5a66f1c8528a963efc2b82a8519f117]
F                     [ARIMA Forecasting] [ARIMA forecasting...] [2008-12-12 12:06:55] [c5a66f1c8528a963efc2b82a8519f117]
-  MPD                    [ARIMA Forecasting] [Arima forecast - ...] [2009-12-16 20:56:07] [557d56ec4b06cd0135c259898de8ce95] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
17.8
17.9
17.4
16.7
16
16.6
19.1
17.8
17.2
18.6
16.3
15.1
19.2
17.7
19.1
18
17.5
17.8
21.1
17.2
19.4
19.8
17.6
16.2
19.5
19.9
20
17.3
18.9
18.6
21.4
18.6
19.8
20.8
19.6
17.7
19.8
22.2
20.7
17.9
20.9
21.2
21.4
23
21.3
23.9
22.4
18.3
22.8
22.3
17.8
16.4
16
16.4
17.7
16.6
16.2
18.3
17.6
15.1

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=68601&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=68601&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68601&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[48])
3617.7-------
3719.8-------
3822.2-------
3920.7-------
4017.9-------
4120.9-------
4221.2-------
4321.4-------
4423-------
4521.3-------
4623.9-------
4722.4-------
4818.3-------
4922.822.679820.296825.73760.46930.99750.96750.9975
5022.324.752221.904828.50680.10030.84590.90860.9996
5117.822.541920.012725.85090.00250.5570.86240.994
5216.419.785517.655322.5380.0080.92130.91030.8549
531623.072920.166827.02452e-040.99950.85940.991
5416.423.465220.322927.83758e-040.99960.84510.9897
5517.723.950120.565328.76410.00550.99890.85040.9893
5616.625.745321.781231.60450.00110.99640.82080.9936
5716.223.674520.197128.70360.00180.99710.82260.9819
5818.327.041522.446834.18680.00820.99850.80560.9918
5917.625.031420.98731.15370.00870.98440.80020.9844
6015.120.006117.320123.74690.00510.89630.81430.8143

\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 & 17.7 & - & - & - & - & - & - & - \tabularnewline
37 & 19.8 & - & - & - & - & - & - & - \tabularnewline
38 & 22.2 & - & - & - & - & - & - & - \tabularnewline
39 & 20.7 & - & - & - & - & - & - & - \tabularnewline
40 & 17.9 & - & - & - & - & - & - & - \tabularnewline
41 & 20.9 & - & - & - & - & - & - & - \tabularnewline
42 & 21.2 & - & - & - & - & - & - & - \tabularnewline
43 & 21.4 & - & - & - & - & - & - & - \tabularnewline
44 & 23 & - & - & - & - & - & - & - \tabularnewline
45 & 21.3 & - & - & - & - & - & - & - \tabularnewline
46 & 23.9 & - & - & - & - & - & - & - \tabularnewline
47 & 22.4 & - & - & - & - & - & - & - \tabularnewline
48 & 18.3 & - & - & - & - & - & - & - \tabularnewline
49 & 22.8 & 22.6798 & 20.2968 & 25.7376 & 0.4693 & 0.9975 & 0.9675 & 0.9975 \tabularnewline
50 & 22.3 & 24.7522 & 21.9048 & 28.5068 & 0.1003 & 0.8459 & 0.9086 & 0.9996 \tabularnewline
51 & 17.8 & 22.5419 & 20.0127 & 25.8509 & 0.0025 & 0.557 & 0.8624 & 0.994 \tabularnewline
52 & 16.4 & 19.7855 & 17.6553 & 22.538 & 0.008 & 0.9213 & 0.9103 & 0.8549 \tabularnewline
53 & 16 & 23.0729 & 20.1668 & 27.0245 & 2e-04 & 0.9995 & 0.8594 & 0.991 \tabularnewline
54 & 16.4 & 23.4652 & 20.3229 & 27.8375 & 8e-04 & 0.9996 & 0.8451 & 0.9897 \tabularnewline
55 & 17.7 & 23.9501 & 20.5653 & 28.7641 & 0.0055 & 0.9989 & 0.8504 & 0.9893 \tabularnewline
56 & 16.6 & 25.7453 & 21.7812 & 31.6045 & 0.0011 & 0.9964 & 0.8208 & 0.9936 \tabularnewline
57 & 16.2 & 23.6745 & 20.1971 & 28.7036 & 0.0018 & 0.9971 & 0.8226 & 0.9819 \tabularnewline
58 & 18.3 & 27.0415 & 22.4468 & 34.1868 & 0.0082 & 0.9985 & 0.8056 & 0.9918 \tabularnewline
59 & 17.6 & 25.0314 & 20.987 & 31.1537 & 0.0087 & 0.9844 & 0.8002 & 0.9844 \tabularnewline
60 & 15.1 & 20.0061 & 17.3201 & 23.7469 & 0.0051 & 0.8963 & 0.8143 & 0.8143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68601&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]17.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]19.8[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]22.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]20.7[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]17.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]20.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]21.2[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]21.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]21.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]23.9[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]22.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]18.3[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]22.8[/C][C]22.6798[/C][C]20.2968[/C][C]25.7376[/C][C]0.4693[/C][C]0.9975[/C][C]0.9675[/C][C]0.9975[/C][/ROW]
[ROW][C]50[/C][C]22.3[/C][C]24.7522[/C][C]21.9048[/C][C]28.5068[/C][C]0.1003[/C][C]0.8459[/C][C]0.9086[/C][C]0.9996[/C][/ROW]
[ROW][C]51[/C][C]17.8[/C][C]22.5419[/C][C]20.0127[/C][C]25.8509[/C][C]0.0025[/C][C]0.557[/C][C]0.8624[/C][C]0.994[/C][/ROW]
[ROW][C]52[/C][C]16.4[/C][C]19.7855[/C][C]17.6553[/C][C]22.538[/C][C]0.008[/C][C]0.9213[/C][C]0.9103[/C][C]0.8549[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]23.0729[/C][C]20.1668[/C][C]27.0245[/C][C]2e-04[/C][C]0.9995[/C][C]0.8594[/C][C]0.991[/C][/ROW]
[ROW][C]54[/C][C]16.4[/C][C]23.4652[/C][C]20.3229[/C][C]27.8375[/C][C]8e-04[/C][C]0.9996[/C][C]0.8451[/C][C]0.9897[/C][/ROW]
[ROW][C]55[/C][C]17.7[/C][C]23.9501[/C][C]20.5653[/C][C]28.7641[/C][C]0.0055[/C][C]0.9989[/C][C]0.8504[/C][C]0.9893[/C][/ROW]
[ROW][C]56[/C][C]16.6[/C][C]25.7453[/C][C]21.7812[/C][C]31.6045[/C][C]0.0011[/C][C]0.9964[/C][C]0.8208[/C][C]0.9936[/C][/ROW]
[ROW][C]57[/C][C]16.2[/C][C]23.6745[/C][C]20.1971[/C][C]28.7036[/C][C]0.0018[/C][C]0.9971[/C][C]0.8226[/C][C]0.9819[/C][/ROW]
[ROW][C]58[/C][C]18.3[/C][C]27.0415[/C][C]22.4468[/C][C]34.1868[/C][C]0.0082[/C][C]0.9985[/C][C]0.8056[/C][C]0.9918[/C][/ROW]
[ROW][C]59[/C][C]17.6[/C][C]25.0314[/C][C]20.987[/C][C]31.1537[/C][C]0.0087[/C][C]0.9844[/C][C]0.8002[/C][C]0.9844[/C][/ROW]
[ROW][C]60[/C][C]15.1[/C][C]20.0061[/C][C]17.3201[/C][C]23.7469[/C][C]0.0051[/C][C]0.8963[/C][C]0.8143[/C][C]0.8143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68601&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68601&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[48])
3617.7-------
3719.8-------
3822.2-------
3920.7-------
4017.9-------
4120.9-------
4221.2-------
4321.4-------
4423-------
4521.3-------
4623.9-------
4722.4-------
4818.3-------
4922.822.679820.296825.73760.46930.99750.96750.9975
5022.324.752221.904828.50680.10030.84590.90860.9996
5117.822.541920.012725.85090.00250.5570.86240.994
5216.419.785517.655322.5380.0080.92130.91030.8549
531623.072920.166827.02452e-040.99950.85940.991
5416.423.465220.322927.83758e-040.99960.84510.9897
5517.723.950120.565328.76410.00550.99890.85040.9893
5616.625.745321.781231.60450.00110.99640.82080.9936
5716.223.674520.197128.70360.00180.99710.82260.9819
5818.327.041522.446834.18680.00820.99850.80560.9918
5917.625.031420.98731.15370.00870.98440.80020.9844
6015.120.006117.320123.74690.00510.89630.81430.8143






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
490.06880.00534e-040.01440.00120.0347
500.0774-0.09910.00836.01340.50110.7079
510.0749-0.21040.017522.48611.87381.3689
520.071-0.17110.014311.46130.95510.9773
530.0874-0.30650.025550.02644.16892.0418
540.0951-0.30110.025149.91784.15982.0396
550.1026-0.2610.021739.06423.25531.8043
560.1161-0.35520.029683.63596.96972.64
570.1084-0.31570.026355.86824.65572.1577
580.1348-0.32330.026976.41386.36782.5235
590.1248-0.29690.024755.22514.60212.1452
600.0954-0.24520.020424.06962.00581.4163

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
49 & 0.0688 & 0.0053 & 4e-04 & 0.0144 & 0.0012 & 0.0347 \tabularnewline
50 & 0.0774 & -0.0991 & 0.0083 & 6.0134 & 0.5011 & 0.7079 \tabularnewline
51 & 0.0749 & -0.2104 & 0.0175 & 22.4861 & 1.8738 & 1.3689 \tabularnewline
52 & 0.071 & -0.1711 & 0.0143 & 11.4613 & 0.9551 & 0.9773 \tabularnewline
53 & 0.0874 & -0.3065 & 0.0255 & 50.0264 & 4.1689 & 2.0418 \tabularnewline
54 & 0.0951 & -0.3011 & 0.0251 & 49.9178 & 4.1598 & 2.0396 \tabularnewline
55 & 0.1026 & -0.261 & 0.0217 & 39.0642 & 3.2553 & 1.8043 \tabularnewline
56 & 0.1161 & -0.3552 & 0.0296 & 83.6359 & 6.9697 & 2.64 \tabularnewline
57 & 0.1084 & -0.3157 & 0.0263 & 55.8682 & 4.6557 & 2.1577 \tabularnewline
58 & 0.1348 & -0.3233 & 0.0269 & 76.4138 & 6.3678 & 2.5235 \tabularnewline
59 & 0.1248 & -0.2969 & 0.0247 & 55.2251 & 4.6021 & 2.1452 \tabularnewline
60 & 0.0954 & -0.2452 & 0.0204 & 24.0696 & 2.0058 & 1.4163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68601&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]0.0688[/C][C]0.0053[/C][C]4e-04[/C][C]0.0144[/C][C]0.0012[/C][C]0.0347[/C][/ROW]
[ROW][C]50[/C][C]0.0774[/C][C]-0.0991[/C][C]0.0083[/C][C]6.0134[/C][C]0.5011[/C][C]0.7079[/C][/ROW]
[ROW][C]51[/C][C]0.0749[/C][C]-0.2104[/C][C]0.0175[/C][C]22.4861[/C][C]1.8738[/C][C]1.3689[/C][/ROW]
[ROW][C]52[/C][C]0.071[/C][C]-0.1711[/C][C]0.0143[/C][C]11.4613[/C][C]0.9551[/C][C]0.9773[/C][/ROW]
[ROW][C]53[/C][C]0.0874[/C][C]-0.3065[/C][C]0.0255[/C][C]50.0264[/C][C]4.1689[/C][C]2.0418[/C][/ROW]
[ROW][C]54[/C][C]0.0951[/C][C]-0.3011[/C][C]0.0251[/C][C]49.9178[/C][C]4.1598[/C][C]2.0396[/C][/ROW]
[ROW][C]55[/C][C]0.1026[/C][C]-0.261[/C][C]0.0217[/C][C]39.0642[/C][C]3.2553[/C][C]1.8043[/C][/ROW]
[ROW][C]56[/C][C]0.1161[/C][C]-0.3552[/C][C]0.0296[/C][C]83.6359[/C][C]6.9697[/C][C]2.64[/C][/ROW]
[ROW][C]57[/C][C]0.1084[/C][C]-0.3157[/C][C]0.0263[/C][C]55.8682[/C][C]4.6557[/C][C]2.1577[/C][/ROW]
[ROW][C]58[/C][C]0.1348[/C][C]-0.3233[/C][C]0.0269[/C][C]76.4138[/C][C]6.3678[/C][C]2.5235[/C][/ROW]
[ROW][C]59[/C][C]0.1248[/C][C]-0.2969[/C][C]0.0247[/C][C]55.2251[/C][C]4.6021[/C][C]2.1452[/C][/ROW]
[ROW][C]60[/C][C]0.0954[/C][C]-0.2452[/C][C]0.0204[/C][C]24.0696[/C][C]2.0058[/C][C]1.4163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68601&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68601&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
490.06880.00534e-040.01440.00120.0347
500.0774-0.09910.00836.01340.50110.7079
510.0749-0.21040.017522.48611.87381.3689
520.071-0.17110.014311.46130.95510.9773
530.0874-0.30650.025550.02644.16892.0418
540.0951-0.30110.025149.91784.15982.0396
550.1026-0.2610.021739.06423.25531.8043
560.1161-0.35520.029683.63596.96972.64
570.1084-0.31570.026355.86824.65572.1577
580.1348-0.32330.026976.41386.36782.5235
590.1248-0.29690.024755.22514.60212.1452
600.0954-0.24520.020424.06962.00581.4163


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 12 ; par2 = -1.1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = -1.1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; 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,fx))
(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.se <- array(0, dim=fx)
perf.mse <- 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$pred[i]
perf.mape[i] = perf.mape[i] + abs(perf.pe[i])
perf.se[i] = (x[nx+i] - forecast$pred[i])^2
perf.mse[i] = perf.mse[i] + perf.se[i]
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 = perf.mape / fx
perf.mse = perf.mse / fx
perf.rmse = sqrt(perf.mse)
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[1:12] <- 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.mape[i],4))
a<-table.element(a,round(perf.se[i],4))
a<-table.element(a,round(perf.mse[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')