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Author's title

Author*Unverified author*
R Software Module--
Title produced by softwareARIMA Forecasting
Date of computationTue, 04 Dec 2012 15:08:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t1354651732z3v0m3fsk2s764u.htm/, Retrieved Fri, 29 Mar 2024 13:05:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196581, Retrieved Fri, 29 Mar 2024 13:05:26 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact99
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 Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
- R PD      [ARIMA Forecasting] [WS9 5 Arima s24] [2010-12-07 16:19:30] [afe9379cca749d06b3d6872e02cc47ed]
- R P         [ARIMA Forecasting] [WS 9 - ARIMA] [2012-12-04 19:05:50] [74be16979710d4c4e7c6647856088456]
- RMP             [ARIMA Forecasting] [] [2012-12-04 20:08:33] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
12008
9169
8788
8417
8247
8197
8236
8253
7733
8366
8626
8863
10102
8463
9114
8563
8872
8301
8301
8278
7736
7973
8268
9476
11100
8962
9173
8738
8459
8078
8411
8291
7810
8616
8312
9692
9911
8915
9452
9112
8472
8230
8384
8625
8221
8649
8625
10443
10357
8586
8892
8329
8101
7922
8120
7838
7735
8406
8209
9451
10041
9411
10405
8467
8464
8102
7627
7513
7510
8291
8064
9383
9706
8579
9474
8318
8213
8059
9111
7708
7680
8014
8007
8718
9486
9113
9025
8476
7952
7759
7835
7600
7651
8319
8812
8630




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196581&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196581&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196581&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







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[72])
609451-------
6110041-------
629411-------
6310405-------
648467-------
658464-------
668102-------
677627-------
687513-------
697510-------
708291-------
718064-------
729383-------
73970610224.97129455.943110993.99930.0930.98410.68040.9841
7485799087.29628210.66349963.92890.12790.08330.23460.2543
7594749816.06938909.273410722.86520.22980.99630.10150.8254
7683188295.52997388.53319202.52680.48060.00540.35550.0094
7782138240.74857333.72679147.77030.47610.43370.31480.0068
7880597963.30337054.79138871.81530.41820.29510.38240.0011
7991117755.3766846.78348663.96860.00170.25620.60912e-04
8077087465.09086556.24198373.93970.30022e-040.45890
8176807478.5646569.55988387.56820.3320.31040.4730
8280148368.63597459.54949277.72240.22230.93120.56650.0144
8380078035.28547126.20448944.36640.47570.51830.47530.0018
8487189113.61638204.777810022.45470.19680.99150.28060.2806
8594869833.4868776.223710890.74830.25970.98070.59340.7982
8691139259.74848164.521110354.97570.39640.34280.88840.4127
87902510293.65669185.869511401.44370.01240.98160.92650.9464
8884768469.6377361.70859577.56550.49550.16290.60570.0531
8979528454.68687346.72739562.64630.18690.4850.66550.0503
9077598090.16276981.51089198.81450.27910.59650.5220.0111
9178357670.35576561.64988779.06150.38550.43770.00540.0012
9276007537.54936428.71048646.38820.45610.29950.38166e-04
9376517531.05426422.13368639.97470.41610.45150.39625e-04
9483198297.15487188.18999406.11970.48460.87330.69160.0275
9588128078.14356969.13939187.14770.09730.33520.550.0106
9686309391.67988282.651710500.70780.08910.84720.88310.5061

\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[72]) \tabularnewline
60 & 9451 & - & - & - & - & - & - & - \tabularnewline
61 & 10041 & - & - & - & - & - & - & - \tabularnewline
62 & 9411 & - & - & - & - & - & - & - \tabularnewline
63 & 10405 & - & - & - & - & - & - & - \tabularnewline
64 & 8467 & - & - & - & - & - & - & - \tabularnewline
65 & 8464 & - & - & - & - & - & - & - \tabularnewline
66 & 8102 & - & - & - & - & - & - & - \tabularnewline
67 & 7627 & - & - & - & - & - & - & - \tabularnewline
68 & 7513 & - & - & - & - & - & - & - \tabularnewline
69 & 7510 & - & - & - & - & - & - & - \tabularnewline
70 & 8291 & - & - & - & - & - & - & - \tabularnewline
71 & 8064 & - & - & - & - & - & - & - \tabularnewline
72 & 9383 & - & - & - & - & - & - & - \tabularnewline
73 & 9706 & 10224.9712 & 9455.9431 & 10993.9993 & 0.093 & 0.9841 & 0.6804 & 0.9841 \tabularnewline
74 & 8579 & 9087.2962 & 8210.6634 & 9963.9289 & 0.1279 & 0.0833 & 0.2346 & 0.2543 \tabularnewline
75 & 9474 & 9816.0693 & 8909.2734 & 10722.8652 & 0.2298 & 0.9963 & 0.1015 & 0.8254 \tabularnewline
76 & 8318 & 8295.5299 & 7388.5331 & 9202.5268 & 0.4806 & 0.0054 & 0.3555 & 0.0094 \tabularnewline
77 & 8213 & 8240.7485 & 7333.7267 & 9147.7703 & 0.4761 & 0.4337 & 0.3148 & 0.0068 \tabularnewline
78 & 8059 & 7963.3033 & 7054.7913 & 8871.8153 & 0.4182 & 0.2951 & 0.3824 & 0.0011 \tabularnewline
79 & 9111 & 7755.376 & 6846.7834 & 8663.9686 & 0.0017 & 0.2562 & 0.6091 & 2e-04 \tabularnewline
80 & 7708 & 7465.0908 & 6556.2419 & 8373.9397 & 0.3002 & 2e-04 & 0.4589 & 0 \tabularnewline
81 & 7680 & 7478.564 & 6569.5598 & 8387.5682 & 0.332 & 0.3104 & 0.473 & 0 \tabularnewline
82 & 8014 & 8368.6359 & 7459.5494 & 9277.7224 & 0.2223 & 0.9312 & 0.5665 & 0.0144 \tabularnewline
83 & 8007 & 8035.2854 & 7126.2044 & 8944.3664 & 0.4757 & 0.5183 & 0.4753 & 0.0018 \tabularnewline
84 & 8718 & 9113.6163 & 8204.7778 & 10022.4547 & 0.1968 & 0.9915 & 0.2806 & 0.2806 \tabularnewline
85 & 9486 & 9833.486 & 8776.2237 & 10890.7483 & 0.2597 & 0.9807 & 0.5934 & 0.7982 \tabularnewline
86 & 9113 & 9259.7484 & 8164.5211 & 10354.9757 & 0.3964 & 0.3428 & 0.8884 & 0.4127 \tabularnewline
87 & 9025 & 10293.6566 & 9185.8695 & 11401.4437 & 0.0124 & 0.9816 & 0.9265 & 0.9464 \tabularnewline
88 & 8476 & 8469.637 & 7361.7085 & 9577.5655 & 0.4955 & 0.1629 & 0.6057 & 0.0531 \tabularnewline
89 & 7952 & 8454.6868 & 7346.7273 & 9562.6463 & 0.1869 & 0.485 & 0.6655 & 0.0503 \tabularnewline
90 & 7759 & 8090.1627 & 6981.5108 & 9198.8145 & 0.2791 & 0.5965 & 0.522 & 0.0111 \tabularnewline
91 & 7835 & 7670.3557 & 6561.6498 & 8779.0615 & 0.3855 & 0.4377 & 0.0054 & 0.0012 \tabularnewline
92 & 7600 & 7537.5493 & 6428.7104 & 8646.3882 & 0.4561 & 0.2995 & 0.3816 & 6e-04 \tabularnewline
93 & 7651 & 7531.0542 & 6422.1336 & 8639.9747 & 0.4161 & 0.4515 & 0.3962 & 5e-04 \tabularnewline
94 & 8319 & 8297.1548 & 7188.1899 & 9406.1197 & 0.4846 & 0.8733 & 0.6916 & 0.0275 \tabularnewline
95 & 8812 & 8078.1435 & 6969.1393 & 9187.1477 & 0.0973 & 0.3352 & 0.55 & 0.0106 \tabularnewline
96 & 8630 & 9391.6798 & 8282.6517 & 10500.7078 & 0.0891 & 0.8472 & 0.8831 & 0.5061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196581&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[72])[/C][/ROW]
[ROW][C]60[/C][C]9451[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]61[/C][C]10041[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]62[/C][C]9411[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]63[/C][C]10405[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]64[/C][C]8467[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]65[/C][C]8464[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]66[/C][C]8102[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]67[/C][C]7627[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]68[/C][C]7513[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]69[/C][C]7510[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]70[/C][C]8291[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]71[/C][C]8064[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]72[/C][C]9383[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]73[/C][C]9706[/C][C]10224.9712[/C][C]9455.9431[/C][C]10993.9993[/C][C]0.093[/C][C]0.9841[/C][C]0.6804[/C][C]0.9841[/C][/ROW]
[ROW][C]74[/C][C]8579[/C][C]9087.2962[/C][C]8210.6634[/C][C]9963.9289[/C][C]0.1279[/C][C]0.0833[/C][C]0.2346[/C][C]0.2543[/C][/ROW]
[ROW][C]75[/C][C]9474[/C][C]9816.0693[/C][C]8909.2734[/C][C]10722.8652[/C][C]0.2298[/C][C]0.9963[/C][C]0.1015[/C][C]0.8254[/C][/ROW]
[ROW][C]76[/C][C]8318[/C][C]8295.5299[/C][C]7388.5331[/C][C]9202.5268[/C][C]0.4806[/C][C]0.0054[/C][C]0.3555[/C][C]0.0094[/C][/ROW]
[ROW][C]77[/C][C]8213[/C][C]8240.7485[/C][C]7333.7267[/C][C]9147.7703[/C][C]0.4761[/C][C]0.4337[/C][C]0.3148[/C][C]0.0068[/C][/ROW]
[ROW][C]78[/C][C]8059[/C][C]7963.3033[/C][C]7054.7913[/C][C]8871.8153[/C][C]0.4182[/C][C]0.2951[/C][C]0.3824[/C][C]0.0011[/C][/ROW]
[ROW][C]79[/C][C]9111[/C][C]7755.376[/C][C]6846.7834[/C][C]8663.9686[/C][C]0.0017[/C][C]0.2562[/C][C]0.6091[/C][C]2e-04[/C][/ROW]
[ROW][C]80[/C][C]7708[/C][C]7465.0908[/C][C]6556.2419[/C][C]8373.9397[/C][C]0.3002[/C][C]2e-04[/C][C]0.4589[/C][C]0[/C][/ROW]
[ROW][C]81[/C][C]7680[/C][C]7478.564[/C][C]6569.5598[/C][C]8387.5682[/C][C]0.332[/C][C]0.3104[/C][C]0.473[/C][C]0[/C][/ROW]
[ROW][C]82[/C][C]8014[/C][C]8368.6359[/C][C]7459.5494[/C][C]9277.7224[/C][C]0.2223[/C][C]0.9312[/C][C]0.5665[/C][C]0.0144[/C][/ROW]
[ROW][C]83[/C][C]8007[/C][C]8035.2854[/C][C]7126.2044[/C][C]8944.3664[/C][C]0.4757[/C][C]0.5183[/C][C]0.4753[/C][C]0.0018[/C][/ROW]
[ROW][C]84[/C][C]8718[/C][C]9113.6163[/C][C]8204.7778[/C][C]10022.4547[/C][C]0.1968[/C][C]0.9915[/C][C]0.2806[/C][C]0.2806[/C][/ROW]
[ROW][C]85[/C][C]9486[/C][C]9833.486[/C][C]8776.2237[/C][C]10890.7483[/C][C]0.2597[/C][C]0.9807[/C][C]0.5934[/C][C]0.7982[/C][/ROW]
[ROW][C]86[/C][C]9113[/C][C]9259.7484[/C][C]8164.5211[/C][C]10354.9757[/C][C]0.3964[/C][C]0.3428[/C][C]0.8884[/C][C]0.4127[/C][/ROW]
[ROW][C]87[/C][C]9025[/C][C]10293.6566[/C][C]9185.8695[/C][C]11401.4437[/C][C]0.0124[/C][C]0.9816[/C][C]0.9265[/C][C]0.9464[/C][/ROW]
[ROW][C]88[/C][C]8476[/C][C]8469.637[/C][C]7361.7085[/C][C]9577.5655[/C][C]0.4955[/C][C]0.1629[/C][C]0.6057[/C][C]0.0531[/C][/ROW]
[ROW][C]89[/C][C]7952[/C][C]8454.6868[/C][C]7346.7273[/C][C]9562.6463[/C][C]0.1869[/C][C]0.485[/C][C]0.6655[/C][C]0.0503[/C][/ROW]
[ROW][C]90[/C][C]7759[/C][C]8090.1627[/C][C]6981.5108[/C][C]9198.8145[/C][C]0.2791[/C][C]0.5965[/C][C]0.522[/C][C]0.0111[/C][/ROW]
[ROW][C]91[/C][C]7835[/C][C]7670.3557[/C][C]6561.6498[/C][C]8779.0615[/C][C]0.3855[/C][C]0.4377[/C][C]0.0054[/C][C]0.0012[/C][/ROW]
[ROW][C]92[/C][C]7600[/C][C]7537.5493[/C][C]6428.7104[/C][C]8646.3882[/C][C]0.4561[/C][C]0.2995[/C][C]0.3816[/C][C]6e-04[/C][/ROW]
[ROW][C]93[/C][C]7651[/C][C]7531.0542[/C][C]6422.1336[/C][C]8639.9747[/C][C]0.4161[/C][C]0.4515[/C][C]0.3962[/C][C]5e-04[/C][/ROW]
[ROW][C]94[/C][C]8319[/C][C]8297.1548[/C][C]7188.1899[/C][C]9406.1197[/C][C]0.4846[/C][C]0.8733[/C][C]0.6916[/C][C]0.0275[/C][/ROW]
[ROW][C]95[/C][C]8812[/C][C]8078.1435[/C][C]6969.1393[/C][C]9187.1477[/C][C]0.0973[/C][C]0.3352[/C][C]0.55[/C][C]0.0106[/C][/ROW]
[ROW][C]96[/C][C]8630[/C][C]9391.6798[/C][C]8282.6517[/C][C]10500.7078[/C][C]0.0891[/C][C]0.8472[/C][C]0.8831[/C][C]0.5061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196581&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196581&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[72])
609451-------
6110041-------
629411-------
6310405-------
648467-------
658464-------
668102-------
677627-------
687513-------
697510-------
708291-------
718064-------
729383-------
73970610224.97129455.943110993.99930.0930.98410.68040.9841
7485799087.29628210.66349963.92890.12790.08330.23460.2543
7594749816.06938909.273410722.86520.22980.99630.10150.8254
7683188295.52997388.53319202.52680.48060.00540.35550.0094
7782138240.74857333.72679147.77030.47610.43370.31480.0068
7880597963.30337054.79138871.81530.41820.29510.38240.0011
7991117755.3766846.78348663.96860.00170.25620.60912e-04
8077087465.09086556.24198373.93970.30022e-040.45890
8176807478.5646569.55988387.56820.3320.31040.4730
8280148368.63597459.54949277.72240.22230.93120.56650.0144
8380078035.28547126.20448944.36640.47570.51830.47530.0018
8487189113.61638204.777810022.45470.19680.99150.28060.2806
8594869833.4868776.223710890.74830.25970.98070.59340.7982
8691139259.74848164.521110354.97570.39640.34280.88840.4127
87902510293.65669185.869511401.44370.01240.98160.92650.9464
8884768469.6377361.70859577.56550.49550.16290.60570.0531
8979528454.68687346.72739562.64630.18690.4850.66550.0503
9077598090.16276981.51089198.81450.27910.59650.5220.0111
9178357670.35576561.64988779.06150.38550.43770.00540.0012
9276007537.54936428.71048646.38820.45610.29950.38166e-04
9376517531.05426422.13368639.97470.41610.45150.39625e-04
9483198297.15487188.18999406.11970.48460.87330.69160.0275
9588128078.14356969.13939187.14770.09730.33520.550.0106
9686309391.67988282.651710500.70780.08910.84720.88310.5061







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
730.0384-0.05080269331.083200
740.0492-0.05590.0533258364.9789263848.0311513.6614
750.0471-0.03480.0472117011.4251214902.4958463.5758
760.05580.00270.0361504.9038161303.0978401.6256
770.0562-0.00340.0295769.9789129196.474359.4391
780.05820.0120.02669157.8603109190.0384330.4392
790.05980.17480.04781837716.4165356122.3781596.7599
800.06210.03250.045959004.8646318982.6889564.7855
810.0620.02690.043840576.4564288048.6631536.7017
820.0554-0.04240.0436125766.6131271820.4581521.364
830.0577-0.00350.04800.0635247182.2404497.1743
840.0509-0.04340.0403156512.2278239626.406489.5165
850.0549-0.03530.0399120746.5225230481.7996480.0852
860.0603-0.01580.038221535.0925215557.0348464.2812
870.0549-0.12320.04381609489.6243308485.8741555.415
880.06678e-040.041140.4874289208.0374537.7807
890.0669-0.05950.0422252693.9854287060.152535.7799
900.0699-0.04090.0422109668.7128277205.0721526.5027
910.07370.02150.041127107.7589264042.0556513.8502
920.07510.00830.03943900.0928251034.9574501.0339
930.07510.01590.038314387.0048239766.0073489.6591
940.06820.00260.0367477.2127228889.2439478.4237
950.070.09080.039538545.3473242352.5528492.2932
960.0602-0.08110.0408580156.0746256427.6995506.3869

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
73 & 0.0384 & -0.0508 & 0 & 269331.0832 & 0 & 0 \tabularnewline
74 & 0.0492 & -0.0559 & 0.0533 & 258364.9789 & 263848.0311 & 513.6614 \tabularnewline
75 & 0.0471 & -0.0348 & 0.0472 & 117011.4251 & 214902.4958 & 463.5758 \tabularnewline
76 & 0.0558 & 0.0027 & 0.0361 & 504.9038 & 161303.0978 & 401.6256 \tabularnewline
77 & 0.0562 & -0.0034 & 0.0295 & 769.9789 & 129196.474 & 359.4391 \tabularnewline
78 & 0.0582 & 0.012 & 0.0266 & 9157.8603 & 109190.0384 & 330.4392 \tabularnewline
79 & 0.0598 & 0.1748 & 0.0478 & 1837716.4165 & 356122.3781 & 596.7599 \tabularnewline
80 & 0.0621 & 0.0325 & 0.0459 & 59004.8646 & 318982.6889 & 564.7855 \tabularnewline
81 & 0.062 & 0.0269 & 0.0438 & 40576.4564 & 288048.6631 & 536.7017 \tabularnewline
82 & 0.0554 & -0.0424 & 0.0436 & 125766.6131 & 271820.4581 & 521.364 \tabularnewline
83 & 0.0577 & -0.0035 & 0.04 & 800.0635 & 247182.2404 & 497.1743 \tabularnewline
84 & 0.0509 & -0.0434 & 0.0403 & 156512.2278 & 239626.406 & 489.5165 \tabularnewline
85 & 0.0549 & -0.0353 & 0.0399 & 120746.5225 & 230481.7996 & 480.0852 \tabularnewline
86 & 0.0603 & -0.0158 & 0.0382 & 21535.0925 & 215557.0348 & 464.2812 \tabularnewline
87 & 0.0549 & -0.1232 & 0.0438 & 1609489.6243 & 308485.8741 & 555.415 \tabularnewline
88 & 0.0667 & 8e-04 & 0.0411 & 40.4874 & 289208.0374 & 537.7807 \tabularnewline
89 & 0.0669 & -0.0595 & 0.0422 & 252693.9854 & 287060.152 & 535.7799 \tabularnewline
90 & 0.0699 & -0.0409 & 0.0422 & 109668.7128 & 277205.0721 & 526.5027 \tabularnewline
91 & 0.0737 & 0.0215 & 0.0411 & 27107.7589 & 264042.0556 & 513.8502 \tabularnewline
92 & 0.0751 & 0.0083 & 0.0394 & 3900.0928 & 251034.9574 & 501.0339 \tabularnewline
93 & 0.0751 & 0.0159 & 0.0383 & 14387.0048 & 239766.0073 & 489.6591 \tabularnewline
94 & 0.0682 & 0.0026 & 0.0367 & 477.2127 & 228889.2439 & 478.4237 \tabularnewline
95 & 0.07 & 0.0908 & 0.039 & 538545.3473 & 242352.5528 & 492.2932 \tabularnewline
96 & 0.0602 & -0.0811 & 0.0408 & 580156.0746 & 256427.6995 & 506.3869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196581&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]73[/C][C]0.0384[/C][C]-0.0508[/C][C]0[/C][C]269331.0832[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]74[/C][C]0.0492[/C][C]-0.0559[/C][C]0.0533[/C][C]258364.9789[/C][C]263848.0311[/C][C]513.6614[/C][/ROW]
[ROW][C]75[/C][C]0.0471[/C][C]-0.0348[/C][C]0.0472[/C][C]117011.4251[/C][C]214902.4958[/C][C]463.5758[/C][/ROW]
[ROW][C]76[/C][C]0.0558[/C][C]0.0027[/C][C]0.0361[/C][C]504.9038[/C][C]161303.0978[/C][C]401.6256[/C][/ROW]
[ROW][C]77[/C][C]0.0562[/C][C]-0.0034[/C][C]0.0295[/C][C]769.9789[/C][C]129196.474[/C][C]359.4391[/C][/ROW]
[ROW][C]78[/C][C]0.0582[/C][C]0.012[/C][C]0.0266[/C][C]9157.8603[/C][C]109190.0384[/C][C]330.4392[/C][/ROW]
[ROW][C]79[/C][C]0.0598[/C][C]0.1748[/C][C]0.0478[/C][C]1837716.4165[/C][C]356122.3781[/C][C]596.7599[/C][/ROW]
[ROW][C]80[/C][C]0.0621[/C][C]0.0325[/C][C]0.0459[/C][C]59004.8646[/C][C]318982.6889[/C][C]564.7855[/C][/ROW]
[ROW][C]81[/C][C]0.062[/C][C]0.0269[/C][C]0.0438[/C][C]40576.4564[/C][C]288048.6631[/C][C]536.7017[/C][/ROW]
[ROW][C]82[/C][C]0.0554[/C][C]-0.0424[/C][C]0.0436[/C][C]125766.6131[/C][C]271820.4581[/C][C]521.364[/C][/ROW]
[ROW][C]83[/C][C]0.0577[/C][C]-0.0035[/C][C]0.04[/C][C]800.0635[/C][C]247182.2404[/C][C]497.1743[/C][/ROW]
[ROW][C]84[/C][C]0.0509[/C][C]-0.0434[/C][C]0.0403[/C][C]156512.2278[/C][C]239626.406[/C][C]489.5165[/C][/ROW]
[ROW][C]85[/C][C]0.0549[/C][C]-0.0353[/C][C]0.0399[/C][C]120746.5225[/C][C]230481.7996[/C][C]480.0852[/C][/ROW]
[ROW][C]86[/C][C]0.0603[/C][C]-0.0158[/C][C]0.0382[/C][C]21535.0925[/C][C]215557.0348[/C][C]464.2812[/C][/ROW]
[ROW][C]87[/C][C]0.0549[/C][C]-0.1232[/C][C]0.0438[/C][C]1609489.6243[/C][C]308485.8741[/C][C]555.415[/C][/ROW]
[ROW][C]88[/C][C]0.0667[/C][C]8e-04[/C][C]0.0411[/C][C]40.4874[/C][C]289208.0374[/C][C]537.7807[/C][/ROW]
[ROW][C]89[/C][C]0.0669[/C][C]-0.0595[/C][C]0.0422[/C][C]252693.9854[/C][C]287060.152[/C][C]535.7799[/C][/ROW]
[ROW][C]90[/C][C]0.0699[/C][C]-0.0409[/C][C]0.0422[/C][C]109668.7128[/C][C]277205.0721[/C][C]526.5027[/C][/ROW]
[ROW][C]91[/C][C]0.0737[/C][C]0.0215[/C][C]0.0411[/C][C]27107.7589[/C][C]264042.0556[/C][C]513.8502[/C][/ROW]
[ROW][C]92[/C][C]0.0751[/C][C]0.0083[/C][C]0.0394[/C][C]3900.0928[/C][C]251034.9574[/C][C]501.0339[/C][/ROW]
[ROW][C]93[/C][C]0.0751[/C][C]0.0159[/C][C]0.0383[/C][C]14387.0048[/C][C]239766.0073[/C][C]489.6591[/C][/ROW]
[ROW][C]94[/C][C]0.0682[/C][C]0.0026[/C][C]0.0367[/C][C]477.2127[/C][C]228889.2439[/C][C]478.4237[/C][/ROW]
[ROW][C]95[/C][C]0.07[/C][C]0.0908[/C][C]0.039[/C][C]538545.3473[/C][C]242352.5528[/C][C]492.2932[/C][/ROW]
[ROW][C]96[/C][C]0.0602[/C][C]-0.0811[/C][C]0.0408[/C][C]580156.0746[/C][C]256427.6995[/C][C]506.3869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196581&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196581&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
730.0384-0.05080269331.083200
740.0492-0.05590.0533258364.9789263848.0311513.6614
750.0471-0.03480.0472117011.4251214902.4958463.5758
760.05580.00270.0361504.9038161303.0978401.6256
770.0562-0.00340.0295769.9789129196.474359.4391
780.05820.0120.02669157.8603109190.0384330.4392
790.05980.17480.04781837716.4165356122.3781596.7599
800.06210.03250.045959004.8646318982.6889564.7855
810.0620.02690.043840576.4564288048.6631536.7017
820.0554-0.04240.0436125766.6131271820.4581521.364
830.0577-0.00350.04800.0635247182.2404497.1743
840.0509-0.04340.0403156512.2278239626.406489.5165
850.0549-0.03530.0399120746.5225230481.7996480.0852
860.0603-0.01580.038221535.0925215557.0348464.2812
870.0549-0.12320.04381609489.6243308485.8741555.415
880.06678e-040.041140.4874289208.0374537.7807
890.0669-0.05950.0422252693.9854287060.152535.7799
900.0699-0.04090.0422109668.7128277205.0721526.5027
910.07370.02150.041127107.7589264042.0556513.8502
920.07510.00830.03943900.0928251034.9574501.0339
930.07510.01590.038314387.0048239766.0073489.6591
940.06820.00260.0367477.2127228889.2439478.4237
950.070.09080.039538545.3473242352.5528492.2932
960.0602-0.08110.0408580156.0746256427.6995506.3869



Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ; 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')