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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationSat, 12 Dec 2009 03:44:25 -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/12/t1260614721il2q0wvxufgeu0m.htm/, Retrieved Thu, 31 Oct 2024 23:40:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66872, Retrieved Thu, 31 Oct 2024 23:40:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Forecasting] [] [2009-12-06 10:44:58] [1e83ffa964db6f7ea6ccc4e7b5acbbff]
-   PD    [ARIMA Forecasting] [review 3 ws 10 ar...] [2009-12-12 10:44:25] [4f297b039e1043ebee7ff7a83b1eaaaa] [Current]
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Dataseries X:
2756.76
2849.27
2921.44
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
3970.1
4138.52
4199.75
4290.89
4443.91
4502.64
4356.98
4591.27
4696.96
4621.4
4562.84
4202.52
4296.49
4435.23
4105.18
4116.68
3844.49
3720.98
3674.4
3857.62
3801.06
3504.37
3032.6
3047.03
2962.34
2197.82
2014.45
1862.83
1905.41
1810.99
1670.07
1864.44
2052.02
2029.6
2070.83
2293.41
2443.27




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=66872&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=66872&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66872&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[36])
243970.1-------
254138.52-------
264199.75-------
274290.89-------
284443.91-------
294502.64-------
304356.98-------
314591.27-------
324696.96-------
334621.4-------
344562.84-------
354202.52-------
364296.49-------
374435.234296.494050.70784542.27220.13430.50.89610.5
384105.184296.493948.90154644.07850.14030.2170.70730.5
394116.684296.493870.78284722.19720.20390.81080.51030.5
403844.494296.493804.92564788.05440.03580.76330.27830.5
413720.984296.493746.90434846.07570.02010.94650.23110.5
423674.44296.493694.44914898.53090.02140.96950.42190.5
433857.624296.493646.21154946.76850.0930.96960.18710.5
443801.064296.493601.3134991.6670.08120.8920.12940.5
453504.374296.493559.14355033.83650.01760.90610.19390.5
463032.64296.493519.25855073.72157e-040.97710.25090.5
473047.034296.493481.32275111.65730.00130.99880.58940.5
482962.344296.493445.07565147.90440.00110.9980.50.5
492197.824296.493410.30975182.670300.99840.37950.5
502014.454296.493376.85735216.1227010.65830.5
511862.834296.493344.57975248.4003010.64440.5
521905.414296.493313.36135279.6187010.81620.5
531810.994296.493283.10415309.8759010.86720.5
541670.074296.493253.72455339.2555010.87890.5
551864.444296.493225.15035367.8297010.7890.5
562052.024296.493197.31875395.6613010.81150.5
572029.64296.493170.17465422.8054010.9160.5
582070.834296.493143.66945449.31061e-040.99990.98420.5
592293.414296.493117.76015475.21994e-040.99990.98110.5
602443.274296.493092.40815500.57190.00130.99940.98510.5

\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[36]) \tabularnewline
24 & 3970.1 & - & - & - & - & - & - & - \tabularnewline
25 & 4138.52 & - & - & - & - & - & - & - \tabularnewline
26 & 4199.75 & - & - & - & - & - & - & - \tabularnewline
27 & 4290.89 & - & - & - & - & - & - & - \tabularnewline
28 & 4443.91 & - & - & - & - & - & - & - \tabularnewline
29 & 4502.64 & - & - & - & - & - & - & - \tabularnewline
30 & 4356.98 & - & - & - & - & - & - & - \tabularnewline
31 & 4591.27 & - & - & - & - & - & - & - \tabularnewline
32 & 4696.96 & - & - & - & - & - & - & - \tabularnewline
33 & 4621.4 & - & - & - & - & - & - & - \tabularnewline
34 & 4562.84 & - & - & - & - & - & - & - \tabularnewline
35 & 4202.52 & - & - & - & - & - & - & - \tabularnewline
36 & 4296.49 & - & - & - & - & - & - & - \tabularnewline
37 & 4435.23 & 4296.49 & 4050.7078 & 4542.2722 & 0.1343 & 0.5 & 0.8961 & 0.5 \tabularnewline
38 & 4105.18 & 4296.49 & 3948.9015 & 4644.0785 & 0.1403 & 0.217 & 0.7073 & 0.5 \tabularnewline
39 & 4116.68 & 4296.49 & 3870.7828 & 4722.1972 & 0.2039 & 0.8108 & 0.5103 & 0.5 \tabularnewline
40 & 3844.49 & 4296.49 & 3804.9256 & 4788.0544 & 0.0358 & 0.7633 & 0.2783 & 0.5 \tabularnewline
41 & 3720.98 & 4296.49 & 3746.9043 & 4846.0757 & 0.0201 & 0.9465 & 0.2311 & 0.5 \tabularnewline
42 & 3674.4 & 4296.49 & 3694.4491 & 4898.5309 & 0.0214 & 0.9695 & 0.4219 & 0.5 \tabularnewline
43 & 3857.62 & 4296.49 & 3646.2115 & 4946.7685 & 0.093 & 0.9696 & 0.1871 & 0.5 \tabularnewline
44 & 3801.06 & 4296.49 & 3601.313 & 4991.667 & 0.0812 & 0.892 & 0.1294 & 0.5 \tabularnewline
45 & 3504.37 & 4296.49 & 3559.1435 & 5033.8365 & 0.0176 & 0.9061 & 0.1939 & 0.5 \tabularnewline
46 & 3032.6 & 4296.49 & 3519.2585 & 5073.7215 & 7e-04 & 0.9771 & 0.2509 & 0.5 \tabularnewline
47 & 3047.03 & 4296.49 & 3481.3227 & 5111.6573 & 0.0013 & 0.9988 & 0.5894 & 0.5 \tabularnewline
48 & 2962.34 & 4296.49 & 3445.0756 & 5147.9044 & 0.0011 & 0.998 & 0.5 & 0.5 \tabularnewline
49 & 2197.82 & 4296.49 & 3410.3097 & 5182.6703 & 0 & 0.9984 & 0.3795 & 0.5 \tabularnewline
50 & 2014.45 & 4296.49 & 3376.8573 & 5216.1227 & 0 & 1 & 0.6583 & 0.5 \tabularnewline
51 & 1862.83 & 4296.49 & 3344.5797 & 5248.4003 & 0 & 1 & 0.6444 & 0.5 \tabularnewline
52 & 1905.41 & 4296.49 & 3313.3613 & 5279.6187 & 0 & 1 & 0.8162 & 0.5 \tabularnewline
53 & 1810.99 & 4296.49 & 3283.1041 & 5309.8759 & 0 & 1 & 0.8672 & 0.5 \tabularnewline
54 & 1670.07 & 4296.49 & 3253.7245 & 5339.2555 & 0 & 1 & 0.8789 & 0.5 \tabularnewline
55 & 1864.44 & 4296.49 & 3225.1503 & 5367.8297 & 0 & 1 & 0.789 & 0.5 \tabularnewline
56 & 2052.02 & 4296.49 & 3197.3187 & 5395.6613 & 0 & 1 & 0.8115 & 0.5 \tabularnewline
57 & 2029.6 & 4296.49 & 3170.1746 & 5422.8054 & 0 & 1 & 0.916 & 0.5 \tabularnewline
58 & 2070.83 & 4296.49 & 3143.6694 & 5449.3106 & 1e-04 & 0.9999 & 0.9842 & 0.5 \tabularnewline
59 & 2293.41 & 4296.49 & 3117.7601 & 5475.2199 & 4e-04 & 0.9999 & 0.9811 & 0.5 \tabularnewline
60 & 2443.27 & 4296.49 & 3092.4081 & 5500.5719 & 0.0013 & 0.9994 & 0.9851 & 0.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66872&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[36])[/C][/ROW]
[ROW][C]24[/C][C]3970.1[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]25[/C][C]4138.52[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]26[/C][C]4199.75[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]27[/C][C]4290.89[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]28[/C][C]4443.91[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]29[/C][C]4502.64[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]30[/C][C]4356.98[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]31[/C][C]4591.27[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]32[/C][C]4696.96[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]33[/C][C]4621.4[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]34[/C][C]4562.84[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]35[/C][C]4202.52[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]36[/C][C]4296.49[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]37[/C][C]4435.23[/C][C]4296.49[/C][C]4050.7078[/C][C]4542.2722[/C][C]0.1343[/C][C]0.5[/C][C]0.8961[/C][C]0.5[/C][/ROW]
[ROW][C]38[/C][C]4105.18[/C][C]4296.49[/C][C]3948.9015[/C][C]4644.0785[/C][C]0.1403[/C][C]0.217[/C][C]0.7073[/C][C]0.5[/C][/ROW]
[ROW][C]39[/C][C]4116.68[/C][C]4296.49[/C][C]3870.7828[/C][C]4722.1972[/C][C]0.2039[/C][C]0.8108[/C][C]0.5103[/C][C]0.5[/C][/ROW]
[ROW][C]40[/C][C]3844.49[/C][C]4296.49[/C][C]3804.9256[/C][C]4788.0544[/C][C]0.0358[/C][C]0.7633[/C][C]0.2783[/C][C]0.5[/C][/ROW]
[ROW][C]41[/C][C]3720.98[/C][C]4296.49[/C][C]3746.9043[/C][C]4846.0757[/C][C]0.0201[/C][C]0.9465[/C][C]0.2311[/C][C]0.5[/C][/ROW]
[ROW][C]42[/C][C]3674.4[/C][C]4296.49[/C][C]3694.4491[/C][C]4898.5309[/C][C]0.0214[/C][C]0.9695[/C][C]0.4219[/C][C]0.5[/C][/ROW]
[ROW][C]43[/C][C]3857.62[/C][C]4296.49[/C][C]3646.2115[/C][C]4946.7685[/C][C]0.093[/C][C]0.9696[/C][C]0.1871[/C][C]0.5[/C][/ROW]
[ROW][C]44[/C][C]3801.06[/C][C]4296.49[/C][C]3601.313[/C][C]4991.667[/C][C]0.0812[/C][C]0.892[/C][C]0.1294[/C][C]0.5[/C][/ROW]
[ROW][C]45[/C][C]3504.37[/C][C]4296.49[/C][C]3559.1435[/C][C]5033.8365[/C][C]0.0176[/C][C]0.9061[/C][C]0.1939[/C][C]0.5[/C][/ROW]
[ROW][C]46[/C][C]3032.6[/C][C]4296.49[/C][C]3519.2585[/C][C]5073.7215[/C][C]7e-04[/C][C]0.9771[/C][C]0.2509[/C][C]0.5[/C][/ROW]
[ROW][C]47[/C][C]3047.03[/C][C]4296.49[/C][C]3481.3227[/C][C]5111.6573[/C][C]0.0013[/C][C]0.9988[/C][C]0.5894[/C][C]0.5[/C][/ROW]
[ROW][C]48[/C][C]2962.34[/C][C]4296.49[/C][C]3445.0756[/C][C]5147.9044[/C][C]0.0011[/C][C]0.998[/C][C]0.5[/C][C]0.5[/C][/ROW]
[ROW][C]49[/C][C]2197.82[/C][C]4296.49[/C][C]3410.3097[/C][C]5182.6703[/C][C]0[/C][C]0.9984[/C][C]0.3795[/C][C]0.5[/C][/ROW]
[ROW][C]50[/C][C]2014.45[/C][C]4296.49[/C][C]3376.8573[/C][C]5216.1227[/C][C]0[/C][C]1[/C][C]0.6583[/C][C]0.5[/C][/ROW]
[ROW][C]51[/C][C]1862.83[/C][C]4296.49[/C][C]3344.5797[/C][C]5248.4003[/C][C]0[/C][C]1[/C][C]0.6444[/C][C]0.5[/C][/ROW]
[ROW][C]52[/C][C]1905.41[/C][C]4296.49[/C][C]3313.3613[/C][C]5279.6187[/C][C]0[/C][C]1[/C][C]0.8162[/C][C]0.5[/C][/ROW]
[ROW][C]53[/C][C]1810.99[/C][C]4296.49[/C][C]3283.1041[/C][C]5309.8759[/C][C]0[/C][C]1[/C][C]0.8672[/C][C]0.5[/C][/ROW]
[ROW][C]54[/C][C]1670.07[/C][C]4296.49[/C][C]3253.7245[/C][C]5339.2555[/C][C]0[/C][C]1[/C][C]0.8789[/C][C]0.5[/C][/ROW]
[ROW][C]55[/C][C]1864.44[/C][C]4296.49[/C][C]3225.1503[/C][C]5367.8297[/C][C]0[/C][C]1[/C][C]0.789[/C][C]0.5[/C][/ROW]
[ROW][C]56[/C][C]2052.02[/C][C]4296.49[/C][C]3197.3187[/C][C]5395.6613[/C][C]0[/C][C]1[/C][C]0.8115[/C][C]0.5[/C][/ROW]
[ROW][C]57[/C][C]2029.6[/C][C]4296.49[/C][C]3170.1746[/C][C]5422.8054[/C][C]0[/C][C]1[/C][C]0.916[/C][C]0.5[/C][/ROW]
[ROW][C]58[/C][C]2070.83[/C][C]4296.49[/C][C]3143.6694[/C][C]5449.3106[/C][C]1e-04[/C][C]0.9999[/C][C]0.9842[/C][C]0.5[/C][/ROW]
[ROW][C]59[/C][C]2293.41[/C][C]4296.49[/C][C]3117.7601[/C][C]5475.2199[/C][C]4e-04[/C][C]0.9999[/C][C]0.9811[/C][C]0.5[/C][/ROW]
[ROW][C]60[/C][C]2443.27[/C][C]4296.49[/C][C]3092.4081[/C][C]5500.5719[/C][C]0.0013[/C][C]0.9994[/C][C]0.9851[/C][C]0.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66872&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66872&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[36])
243970.1-------
254138.52-------
264199.75-------
274290.89-------
284443.91-------
294502.64-------
304356.98-------
314591.27-------
324696.96-------
334621.4-------
344562.84-------
354202.52-------
364296.49-------
374435.234296.494050.70784542.27220.13430.50.89610.5
384105.184296.493948.90154644.07850.14030.2170.70730.5
394116.684296.493870.78284722.19720.20390.81080.51030.5
403844.494296.493804.92564788.05440.03580.76330.27830.5
413720.984296.493746.90434846.07570.02010.94650.23110.5
423674.44296.493694.44914898.53090.02140.96950.42190.5
433857.624296.493646.21154946.76850.0930.96960.18710.5
443801.064296.493601.3134991.6670.08120.8920.12940.5
453504.374296.493559.14355033.83650.01760.90610.19390.5
463032.64296.493519.25855073.72157e-040.97710.25090.5
473047.034296.493481.32275111.65730.00130.99880.58940.5
482962.344296.493445.07565147.90440.00110.9980.50.5
492197.824296.493410.30975182.670300.99840.37950.5
502014.454296.493376.85735216.1227010.65830.5
511862.834296.493344.57975248.4003010.64440.5
521905.414296.493313.36135279.6187010.81620.5
531810.994296.493283.10415309.8759010.86720.5
541670.074296.493253.72455339.2555010.87890.5
551864.444296.493225.15035367.8297010.7890.5
562052.024296.493197.31875395.6613010.81150.5
572029.64296.493170.17465422.8054010.9160.5
582070.834296.493143.66945449.31061e-040.99990.98420.5
592293.414296.493117.76015475.21994e-040.99990.98110.5
602443.274296.493092.40815500.57190.00130.99940.98510.5







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPESq.EMSERMSE
370.02920.03230.001319248.7876802.032828.3202
380.0413-0.04450.001936599.51611524.979839.051
390.0506-0.04190.001732331.63611347.151536.7036
400.0584-0.10520.00442043048512.666792.2641
410.0653-0.13390.0056331211.760113800.49117.4755
420.0715-0.14480.006386995.968116124.832126.9836
430.0772-0.10210.0043192606.87698025.286589.584
440.0826-0.11530.0048245450.884910227.1202101.1292
450.0876-0.18440.0077627454.094426143.9206161.6908
460.0923-0.29420.01231597417.932166559.0805257.9905
470.0968-0.29080.01211561150.291665047.9288255.045
480.1011-0.31050.01291779956.222574164.8426272.3322
490.1052-0.48850.02044404415.7689183517.3237428.3892
500.1092-0.53110.02215207706.5616216987.7734465.8195
510.113-0.56640.02365922700.9956246779.2081496.7688
520.1167-0.55650.02325717263.5664238219.3153488.0772
530.1203-0.57850.02416177710.25257404.5938507.3506
540.1238-0.61130.02556898082.0164287420.084536.1157
550.1272-0.56610.02365914867.2025246452.8001496.4401
560.1305-0.52240.02185037645.5809209901.8992458.1505
570.1337-0.52760.0225138790.2721214116.2613462.727
580.1369-0.5180.02164953562.4356206398.4348454.3109
590.14-0.46620.01944012329.4864167180.3953408.877
600.143-0.43130.0183434424.3684143101.0153378.2869

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & Sq.E & MSE & RMSE \tabularnewline
37 & 0.0292 & 0.0323 & 0.0013 & 19248.7876 & 802.0328 & 28.3202 \tabularnewline
38 & 0.0413 & -0.0445 & 0.0019 & 36599.5161 & 1524.9798 & 39.051 \tabularnewline
39 & 0.0506 & -0.0419 & 0.0017 & 32331.6361 & 1347.1515 & 36.7036 \tabularnewline
40 & 0.0584 & -0.1052 & 0.0044 & 204304 & 8512.6667 & 92.2641 \tabularnewline
41 & 0.0653 & -0.1339 & 0.0056 & 331211.7601 & 13800.49 & 117.4755 \tabularnewline
42 & 0.0715 & -0.1448 & 0.006 & 386995.9681 & 16124.832 & 126.9836 \tabularnewline
43 & 0.0772 & -0.1021 & 0.0043 & 192606.8769 & 8025.2865 & 89.584 \tabularnewline
44 & 0.0826 & -0.1153 & 0.0048 & 245450.8849 & 10227.1202 & 101.1292 \tabularnewline
45 & 0.0876 & -0.1844 & 0.0077 & 627454.0944 & 26143.9206 & 161.6908 \tabularnewline
46 & 0.0923 & -0.2942 & 0.0123 & 1597417.9321 & 66559.0805 & 257.9905 \tabularnewline
47 & 0.0968 & -0.2908 & 0.0121 & 1561150.2916 & 65047.9288 & 255.045 \tabularnewline
48 & 0.1011 & -0.3105 & 0.0129 & 1779956.2225 & 74164.8426 & 272.3322 \tabularnewline
49 & 0.1052 & -0.4885 & 0.0204 & 4404415.7689 & 183517.3237 & 428.3892 \tabularnewline
50 & 0.1092 & -0.5311 & 0.0221 & 5207706.5616 & 216987.7734 & 465.8195 \tabularnewline
51 & 0.113 & -0.5664 & 0.0236 & 5922700.9956 & 246779.2081 & 496.7688 \tabularnewline
52 & 0.1167 & -0.5565 & 0.0232 & 5717263.5664 & 238219.3153 & 488.0772 \tabularnewline
53 & 0.1203 & -0.5785 & 0.0241 & 6177710.25 & 257404.5938 & 507.3506 \tabularnewline
54 & 0.1238 & -0.6113 & 0.0255 & 6898082.0164 & 287420.084 & 536.1157 \tabularnewline
55 & 0.1272 & -0.5661 & 0.0236 & 5914867.2025 & 246452.8001 & 496.4401 \tabularnewline
56 & 0.1305 & -0.5224 & 0.0218 & 5037645.5809 & 209901.8992 & 458.1505 \tabularnewline
57 & 0.1337 & -0.5276 & 0.022 & 5138790.2721 & 214116.2613 & 462.727 \tabularnewline
58 & 0.1369 & -0.518 & 0.0216 & 4953562.4356 & 206398.4348 & 454.3109 \tabularnewline
59 & 0.14 & -0.4662 & 0.0194 & 4012329.4864 & 167180.3953 & 408.877 \tabularnewline
60 & 0.143 & -0.4313 & 0.018 & 3434424.3684 & 143101.0153 & 378.2869 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66872&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]37[/C][C]0.0292[/C][C]0.0323[/C][C]0.0013[/C][C]19248.7876[/C][C]802.0328[/C][C]28.3202[/C][/ROW]
[ROW][C]38[/C][C]0.0413[/C][C]-0.0445[/C][C]0.0019[/C][C]36599.5161[/C][C]1524.9798[/C][C]39.051[/C][/ROW]
[ROW][C]39[/C][C]0.0506[/C][C]-0.0419[/C][C]0.0017[/C][C]32331.6361[/C][C]1347.1515[/C][C]36.7036[/C][/ROW]
[ROW][C]40[/C][C]0.0584[/C][C]-0.1052[/C][C]0.0044[/C][C]204304[/C][C]8512.6667[/C][C]92.2641[/C][/ROW]
[ROW][C]41[/C][C]0.0653[/C][C]-0.1339[/C][C]0.0056[/C][C]331211.7601[/C][C]13800.49[/C][C]117.4755[/C][/ROW]
[ROW][C]42[/C][C]0.0715[/C][C]-0.1448[/C][C]0.006[/C][C]386995.9681[/C][C]16124.832[/C][C]126.9836[/C][/ROW]
[ROW][C]43[/C][C]0.0772[/C][C]-0.1021[/C][C]0.0043[/C][C]192606.8769[/C][C]8025.2865[/C][C]89.584[/C][/ROW]
[ROW][C]44[/C][C]0.0826[/C][C]-0.1153[/C][C]0.0048[/C][C]245450.8849[/C][C]10227.1202[/C][C]101.1292[/C][/ROW]
[ROW][C]45[/C][C]0.0876[/C][C]-0.1844[/C][C]0.0077[/C][C]627454.0944[/C][C]26143.9206[/C][C]161.6908[/C][/ROW]
[ROW][C]46[/C][C]0.0923[/C][C]-0.2942[/C][C]0.0123[/C][C]1597417.9321[/C][C]66559.0805[/C][C]257.9905[/C][/ROW]
[ROW][C]47[/C][C]0.0968[/C][C]-0.2908[/C][C]0.0121[/C][C]1561150.2916[/C][C]65047.9288[/C][C]255.045[/C][/ROW]
[ROW][C]48[/C][C]0.1011[/C][C]-0.3105[/C][C]0.0129[/C][C]1779956.2225[/C][C]74164.8426[/C][C]272.3322[/C][/ROW]
[ROW][C]49[/C][C]0.1052[/C][C]-0.4885[/C][C]0.0204[/C][C]4404415.7689[/C][C]183517.3237[/C][C]428.3892[/C][/ROW]
[ROW][C]50[/C][C]0.1092[/C][C]-0.5311[/C][C]0.0221[/C][C]5207706.5616[/C][C]216987.7734[/C][C]465.8195[/C][/ROW]
[ROW][C]51[/C][C]0.113[/C][C]-0.5664[/C][C]0.0236[/C][C]5922700.9956[/C][C]246779.2081[/C][C]496.7688[/C][/ROW]
[ROW][C]52[/C][C]0.1167[/C][C]-0.5565[/C][C]0.0232[/C][C]5717263.5664[/C][C]238219.3153[/C][C]488.0772[/C][/ROW]
[ROW][C]53[/C][C]0.1203[/C][C]-0.5785[/C][C]0.0241[/C][C]6177710.25[/C][C]257404.5938[/C][C]507.3506[/C][/ROW]
[ROW][C]54[/C][C]0.1238[/C][C]-0.6113[/C][C]0.0255[/C][C]6898082.0164[/C][C]287420.084[/C][C]536.1157[/C][/ROW]
[ROW][C]55[/C][C]0.1272[/C][C]-0.5661[/C][C]0.0236[/C][C]5914867.2025[/C][C]246452.8001[/C][C]496.4401[/C][/ROW]
[ROW][C]56[/C][C]0.1305[/C][C]-0.5224[/C][C]0.0218[/C][C]5037645.5809[/C][C]209901.8992[/C][C]458.1505[/C][/ROW]
[ROW][C]57[/C][C]0.1337[/C][C]-0.5276[/C][C]0.022[/C][C]5138790.2721[/C][C]214116.2613[/C][C]462.727[/C][/ROW]
[ROW][C]58[/C][C]0.1369[/C][C]-0.518[/C][C]0.0216[/C][C]4953562.4356[/C][C]206398.4348[/C][C]454.3109[/C][/ROW]
[ROW][C]59[/C][C]0.14[/C][C]-0.4662[/C][C]0.0194[/C][C]4012329.4864[/C][C]167180.3953[/C][C]408.877[/C][/ROW]
[ROW][C]60[/C][C]0.143[/C][C]-0.4313[/C][C]0.018[/C][C]3434424.3684[/C][C]143101.0153[/C][C]378.2869[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66872&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66872&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
370.02920.03230.001319248.7876802.032828.3202
380.0413-0.04450.001936599.51611524.979839.051
390.0506-0.04190.001732331.63611347.151536.7036
400.0584-0.10520.00442043048512.666792.2641
410.0653-0.13390.0056331211.760113800.49117.4755
420.0715-0.14480.006386995.968116124.832126.9836
430.0772-0.10210.0043192606.87698025.286589.584
440.0826-0.11530.0048245450.884910227.1202101.1292
450.0876-0.18440.0077627454.094426143.9206161.6908
460.0923-0.29420.01231597417.932166559.0805257.9905
470.0968-0.29080.01211561150.291665047.9288255.045
480.1011-0.31050.01291779956.222574164.8426272.3322
490.1052-0.48850.02044404415.7689183517.3237428.3892
500.1092-0.53110.02215207706.5616216987.7734465.8195
510.113-0.56640.02365922700.9956246779.2081496.7688
520.1167-0.55650.02325717263.5664238219.3153488.0772
530.1203-0.57850.02416177710.25257404.5938507.3506
540.1238-0.61130.02556898082.0164287420.084536.1157
550.1272-0.56610.02365914867.2025246452.8001496.4401
560.1305-0.52240.02185037645.5809209901.8992458.1505
570.1337-0.52760.0225138790.2721214116.2613462.727
580.1369-0.5180.02164953562.4356206398.4348454.3109
590.14-0.46620.01944012329.4864167180.3953408.877
600.143-0.43130.0183434424.3684143101.0153378.2869



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