FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 21 Dec 2010 19:37:30 +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/21/t1292960214znnwkg57arr0tv6.htm/, Retrieved Sun, 19 May 2024 18:22:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113887, Retrieved Sun, 19 May 2024 18:22:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Standard Deviation-Mean Plot] [SMP prof bach] [2008-12-15 22:25:20] [bc937651ef42bf891200cf0e0edc7238]
- RM    [Variance Reduction Matrix] [VRM prof bach] [2008-12-15 22:31:00] [bc937651ef42bf891200cf0e0edc7238]
- RMP     [(Partial) Autocorrelation Function] [ARIMA Prof bach A...] [2008-12-15 22:38:57] [bc937651ef42bf891200cf0e0edc7238]
- RMP       [ARIMA Backward Selection] [Arima backward se...] [2008-12-19 17:26:16] [bc937651ef42bf891200cf0e0edc7238]
- RMP         [ARIMA Forecasting] [ARIMA forecast pr...] [2008-12-20 11:34:44] [bc937651ef42bf891200cf0e0edc7238]
-  MPD            [ARIMA Forecasting] [] [2010-12-21 19:37:30] [d1991ab4912b5ede0ff54c26afa5d84c] [Current]
-    D              [ARIMA Forecasting] [] [2010-12-22 16:40:18] [94f4aa1c01e87d8321fffb341ed4df07]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2981,85
3080,58
3106,22
3119,31
3061,26
3097,31
3161,69
3257,16
3277,01
3295,32
3363,99
3494,17
3667,03
3813,06
3917,96
3895,51
3801,06
3570,12
3701,61
3862,27
3970,10
4138,52
4199,75
4290,89
4443,91
4502,64
4356,98
4591,27
4696,96
4621,40
4562,84
4202,52
4296,49
4435,23
4105,18
4116,68
3844,49
3720,98
3674,40
3857,62
3801,06
3504,37
3032,60
3047,03
2962,34
2197,82
2014,45
1862,83
1905,41
1810,99
1670,07
1864,44
2052,02
2029,60
2070,83
2293,41
2443,27
2513,17
2466,92
2502,66
2539,91

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=113887&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=113887&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113887&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[49])
373844.49-------
383720.98-------
393674.4-------
403857.62-------
413801.06-------
423504.37-------
433032.6-------
443047.03-------
452962.34-------
462197.82-------
472014.45-------
481862.83-------
491905.41-------
501810.991917.21581558.1232276.30860.2810.525700.5257
511670.071920.48921337.98352502.99480.19970.643700.5202
521864.441921.39671162.60662680.18690.44150.741900.5165
532052.021921.64841016.26762827.02920.38890.549300.514
542029.61921.7181889.35822954.07810.41890.40230.00130.5124
552070.831921.7375776.13563067.33940.39930.42680.02870.5111
562293.411921.7428673.06253170.42320.27980.40750.03870.5102
572443.271921.7443577.85213265.63660.22340.29390.06460.5095
582513.171921.7447488.94943354.54010.20920.23780.35280.5089
592466.921921.7449405.24823438.24150.24050.22230.45230.5084
602502.661921.7449325.93073517.5590.23780.25160.52880.508
612539.911921.7449250.37313593.11670.23430.24790.50760.5076

\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 & 3844.49 & - & - & - & - & - & - & - \tabularnewline
38 & 3720.98 & - & - & - & - & - & - & - \tabularnewline
39 & 3674.4 & - & - & - & - & - & - & - \tabularnewline
40 & 3857.62 & - & - & - & - & - & - & - \tabularnewline
41 & 3801.06 & - & - & - & - & - & - & - \tabularnewline
42 & 3504.37 & - & - & - & - & - & - & - \tabularnewline
43 & 3032.6 & - & - & - & - & - & - & - \tabularnewline
44 & 3047.03 & - & - & - & - & - & - & - \tabularnewline
45 & 2962.34 & - & - & - & - & - & - & - \tabularnewline
46 & 2197.82 & - & - & - & - & - & - & - \tabularnewline
47 & 2014.45 & - & - & - & - & - & - & - \tabularnewline
48 & 1862.83 & - & - & - & - & - & - & - \tabularnewline
49 & 1905.41 & - & - & - & - & - & - & - \tabularnewline
50 & 1810.99 & 1917.2158 & 1558.123 & 2276.3086 & 0.281 & 0.5257 & 0 & 0.5257 \tabularnewline
51 & 1670.07 & 1920.4892 & 1337.9835 & 2502.9948 & 0.1997 & 0.6437 & 0 & 0.5202 \tabularnewline
52 & 1864.44 & 1921.3967 & 1162.6066 & 2680.1869 & 0.4415 & 0.7419 & 0 & 0.5165 \tabularnewline
53 & 2052.02 & 1921.6484 & 1016.2676 & 2827.0292 & 0.3889 & 0.5493 & 0 & 0.514 \tabularnewline
54 & 2029.6 & 1921.7181 & 889.3582 & 2954.0781 & 0.4189 & 0.4023 & 0.0013 & 0.5124 \tabularnewline
55 & 2070.83 & 1921.7375 & 776.1356 & 3067.3394 & 0.3993 & 0.4268 & 0.0287 & 0.5111 \tabularnewline
56 & 2293.41 & 1921.7428 & 673.0625 & 3170.4232 & 0.2798 & 0.4075 & 0.0387 & 0.5102 \tabularnewline
57 & 2443.27 & 1921.7443 & 577.8521 & 3265.6366 & 0.2234 & 0.2939 & 0.0646 & 0.5095 \tabularnewline
58 & 2513.17 & 1921.7447 & 488.9494 & 3354.5401 & 0.2092 & 0.2378 & 0.3528 & 0.5089 \tabularnewline
59 & 2466.92 & 1921.7449 & 405.2482 & 3438.2415 & 0.2405 & 0.2223 & 0.4523 & 0.5084 \tabularnewline
60 & 2502.66 & 1921.7449 & 325.9307 & 3517.559 & 0.2378 & 0.2516 & 0.5288 & 0.508 \tabularnewline
61 & 2539.91 & 1921.7449 & 250.3731 & 3593.1167 & 0.2343 & 0.2479 & 0.5076 & 0.5076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113887&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]3844.49[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]38[/C][C]3720.98[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]39[/C][C]3674.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]40[/C][C]3857.62[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]41[/C][C]3801.06[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]42[/C][C]3504.37[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]43[/C][C]3032.6[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]44[/C][C]3047.03[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]45[/C][C]2962.34[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]46[/C][C]2197.82[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]47[/C][C]2014.45[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]48[/C][C]1862.83[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]49[/C][C]1905.41[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]50[/C][C]1810.99[/C][C]1917.2158[/C][C]1558.123[/C][C]2276.3086[/C][C]0.281[/C][C]0.5257[/C][C]0[/C][C]0.5257[/C][/ROW]
[ROW][C]51[/C][C]1670.07[/C][C]1920.4892[/C][C]1337.9835[/C][C]2502.9948[/C][C]0.1997[/C][C]0.6437[/C][C]0[/C][C]0.5202[/C][/ROW]
[ROW][C]52[/C][C]1864.44[/C][C]1921.3967[/C][C]1162.6066[/C][C]2680.1869[/C][C]0.4415[/C][C]0.7419[/C][C]0[/C][C]0.5165[/C][/ROW]
[ROW][C]53[/C][C]2052.02[/C][C]1921.6484[/C][C]1016.2676[/C][C]2827.0292[/C][C]0.3889[/C][C]0.5493[/C][C]0[/C][C]0.514[/C][/ROW]
[ROW][C]54[/C][C]2029.6[/C][C]1921.7181[/C][C]889.3582[/C][C]2954.0781[/C][C]0.4189[/C][C]0.4023[/C][C]0.0013[/C][C]0.5124[/C][/ROW]
[ROW][C]55[/C][C]2070.83[/C][C]1921.7375[/C][C]776.1356[/C][C]3067.3394[/C][C]0.3993[/C][C]0.4268[/C][C]0.0287[/C][C]0.5111[/C][/ROW]
[ROW][C]56[/C][C]2293.41[/C][C]1921.7428[/C][C]673.0625[/C][C]3170.4232[/C][C]0.2798[/C][C]0.4075[/C][C]0.0387[/C][C]0.5102[/C][/ROW]
[ROW][C]57[/C][C]2443.27[/C][C]1921.7443[/C][C]577.8521[/C][C]3265.6366[/C][C]0.2234[/C][C]0.2939[/C][C]0.0646[/C][C]0.5095[/C][/ROW]
[ROW][C]58[/C][C]2513.17[/C][C]1921.7447[/C][C]488.9494[/C][C]3354.5401[/C][C]0.2092[/C][C]0.2378[/C][C]0.3528[/C][C]0.5089[/C][/ROW]
[ROW][C]59[/C][C]2466.92[/C][C]1921.7449[/C][C]405.2482[/C][C]3438.2415[/C][C]0.2405[/C][C]0.2223[/C][C]0.4523[/C][C]0.5084[/C][/ROW]
[ROW][C]60[/C][C]2502.66[/C][C]1921.7449[/C][C]325.9307[/C][C]3517.559[/C][C]0.2378[/C][C]0.2516[/C][C]0.5288[/C][C]0.508[/C][/ROW]
[ROW][C]61[/C][C]2539.91[/C][C]1921.7449[/C][C]250.3731[/C][C]3593.1167[/C][C]0.2343[/C][C]0.2479[/C][C]0.5076[/C][C]0.5076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113887&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113887&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[49])
373844.49-------
383720.98-------
393674.4-------
403857.62-------
413801.06-------
423504.37-------
433032.6-------
443047.03-------
452962.34-------
462197.82-------
472014.45-------
481862.83-------
491905.41-------
501810.991917.21581558.1232276.30860.2810.525700.5257
511670.071920.48921337.98352502.99480.19970.643700.5202
521864.441921.39671162.60662680.18690.44150.741900.5165
532052.021921.64841016.26762827.02920.38890.549300.514
542029.61921.7181889.35822954.07810.41890.40230.00130.5124
552070.831921.7375776.13563067.33940.39930.42680.02870.5111
562293.411921.7428673.06253170.42320.27980.40750.03870.5102
572443.271921.7443577.85213265.63660.22340.29390.06460.5095
582513.171921.7447488.94943354.54010.20920.23780.35280.5089
592466.921921.7449405.24823438.24150.24050.22230.45230.5084
602502.661921.7449325.93073517.5590.23780.25160.52880.508
612539.911921.7449250.37313593.11670.23430.24790.50760.5076






Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
500.0956-0.05540.004611283.9303940.327530.6648
510.1548-0.13040.010962709.75875225.813272.2898
520.2015-0.02960.00253244.0697270.339116.442
530.24040.06780.005716996.76171416.396837.635
540.27410.05610.004711638.4957969.874631.1428
550.30410.07760.006522228.57821852.381543.0393
560.33150.19340.0161138136.47211511.3727107.2911
570.35680.27140.0226271989.019422665.7516150.5515
580.38040.30780.0256349783.829729148.6525170.7298
590.40260.28370.0236297215.931624767.9943157.3785
600.42370.30230.0252337462.361328121.8634167.6957
610.44370.32170.0268382128.088431844.0074178.4489

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
50 & 0.0956 & -0.0554 & 0.0046 & 11283.9303 & 940.3275 & 30.6648 \tabularnewline
51 & 0.1548 & -0.1304 & 0.0109 & 62709.7587 & 5225.8132 & 72.2898 \tabularnewline
52 & 0.2015 & -0.0296 & 0.0025 & 3244.0697 & 270.3391 & 16.442 \tabularnewline
53 & 0.2404 & 0.0678 & 0.0057 & 16996.7617 & 1416.3968 & 37.635 \tabularnewline
54 & 0.2741 & 0.0561 & 0.0047 & 11638.4957 & 969.8746 & 31.1428 \tabularnewline
55 & 0.3041 & 0.0776 & 0.0065 & 22228.5782 & 1852.3815 & 43.0393 \tabularnewline
56 & 0.3315 & 0.1934 & 0.0161 & 138136.472 & 11511.3727 & 107.2911 \tabularnewline
57 & 0.3568 & 0.2714 & 0.0226 & 271989.0194 & 22665.7516 & 150.5515 \tabularnewline
58 & 0.3804 & 0.3078 & 0.0256 & 349783.8297 & 29148.6525 & 170.7298 \tabularnewline
59 & 0.4026 & 0.2837 & 0.0236 & 297215.9316 & 24767.9943 & 157.3785 \tabularnewline
60 & 0.4237 & 0.3023 & 0.0252 & 337462.3613 & 28121.8634 & 167.6957 \tabularnewline
61 & 0.4437 & 0.3217 & 0.0268 & 382128.0884 & 31844.0074 & 178.4489 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113887&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]0.0956[/C][C]-0.0554[/C][C]0.0046[/C][C]11283.9303[/C][C]940.3275[/C][C]30.6648[/C][/ROW]
[ROW][C]51[/C][C]0.1548[/C][C]-0.1304[/C][C]0.0109[/C][C]62709.7587[/C][C]5225.8132[/C][C]72.2898[/C][/ROW]
[ROW][C]52[/C][C]0.2015[/C][C]-0.0296[/C][C]0.0025[/C][C]3244.0697[/C][C]270.3391[/C][C]16.442[/C][/ROW]
[ROW][C]53[/C][C]0.2404[/C][C]0.0678[/C][C]0.0057[/C][C]16996.7617[/C][C]1416.3968[/C][C]37.635[/C][/ROW]
[ROW][C]54[/C][C]0.2741[/C][C]0.0561[/C][C]0.0047[/C][C]11638.4957[/C][C]969.8746[/C][C]31.1428[/C][/ROW]
[ROW][C]55[/C][C]0.3041[/C][C]0.0776[/C][C]0.0065[/C][C]22228.5782[/C][C]1852.3815[/C][C]43.0393[/C][/ROW]
[ROW][C]56[/C][C]0.3315[/C][C]0.1934[/C][C]0.0161[/C][C]138136.472[/C][C]11511.3727[/C][C]107.2911[/C][/ROW]
[ROW][C]57[/C][C]0.3568[/C][C]0.2714[/C][C]0.0226[/C][C]271989.0194[/C][C]22665.7516[/C][C]150.5515[/C][/ROW]
[ROW][C]58[/C][C]0.3804[/C][C]0.3078[/C][C]0.0256[/C][C]349783.8297[/C][C]29148.6525[/C][C]170.7298[/C][/ROW]
[ROW][C]59[/C][C]0.4026[/C][C]0.2837[/C][C]0.0236[/C][C]297215.9316[/C][C]24767.9943[/C][C]157.3785[/C][/ROW]
[ROW][C]60[/C][C]0.4237[/C][C]0.3023[/C][C]0.0252[/C][C]337462.3613[/C][C]28121.8634[/C][C]167.6957[/C][/ROW]
[ROW][C]61[/C][C]0.4437[/C][C]0.3217[/C][C]0.0268[/C][C]382128.0884[/C][C]31844.0074[/C][C]178.4489[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113887&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113887&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
500.0956-0.05540.004611283.9303940.327530.6648
510.1548-0.13040.010962709.75875225.813272.2898
520.2015-0.02960.00253244.0697270.339116.442
530.24040.06780.005716996.76171416.396837.635
540.27410.05610.004711638.4957969.874631.1428
550.30410.07760.006522228.57821852.381543.0393
560.33150.19340.0161138136.47211511.3727107.2911
570.35680.27140.0226271989.019422665.7516150.5515
580.38040.30780.0256349783.829729148.6525170.7298
590.40260.28370.0236297215.931624767.9943157.3785
600.42370.30230.0252337462.361328121.8634167.6957
610.44370.32170.0268382128.088431844.0074178.4489


Figures (Output of Computation)

Input Parameters & R Code

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