## Free Statistics

of Irreproducible Research!

Author's title
Author*Unverified author*
R Software Modulerwasp_arimaforecasting.wasp
Title produced by softwareARIMA Forecasting
Date of computationThu, 12 Dec 2013 11:19:13 -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/2013/Dec/12/t13868652989000zsyzceo9axq.htm/, Retrieved Sun, 05 Dec 2021 18:02:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232258, Retrieved Sun, 05 Dec 2021 18:02:32 +0000
QR Codes:

Original text written by user:drew
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact46
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
1750
1800
1600
1750
2100
1900
1500
1700
2500
1800
1600
1500
1500
1450
1450
1600
1800
1500
1500
1545
1550
1600
1600
1500
1550


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'George Udny Yule' @ yule.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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232258&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232258&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232258&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'George Udny Yule' @ yule.wessa.net

 Univariate ARIMA Extrapolation Forecast 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[1]) 0 - - - - - - - 1 1750 - - - - - - - 2 1800 0 -3430 3430 0.1518 0.1587 0.1587 0.1587 3 1600 0 -3430 3430 0.1803 0.1518 0.1518 0.1587 4 1750 0 -3430 3430 0.1587 0.1803 0.1803 0.1587 5 2100 0 -3430 3430 0.1151 0.1587 0.1587 0.1587 6 1900 0 -3430 3430 0.1388 0.1151 0.1151 0.1587 7 1500 0 -3430 3430 0.1957 0.1388 0.1388 0.1587 8 1700 0 -3430 3430 0.1657 0.1957 0.1957 0.1587 9 2500 0 -3430 3430 0.0766 0.1657 0.1657 0.1587 10 1800 0 -3430 3430 0.1518 0.0766 0.0766 0.1587 11 1600 0 -3430 3430 0.1803 0.1518 0.1518 0.1587 12 1500 0 -3430 3430 0.1957 0.1803 0.1803 0.1587 13 1500 0 -3430 3430 0.1957 0.1957 0.1957 0.1587 14 1450 0 -3430 3430 0.2037 0.1957 0.1957 0.1587 15 1450 0 -3430 3430 0.2037 0.2037 0.2037 0.1587 16 1600 0 -3430 3430 0.1803 0.2037 0.2037 0.1587 17 1800 0 -3430 3430 0.1518 0.1803 0.1803 0.1587 18 1500 0 -3430 3430 0.1957 0.1518 0.1518 0.1587 19 1500 0 -3430 3430 0.1957 0.1957 0.1957 0.1587 20 1545 0 -3430 3430 0.1887 0.1957 0.1957 0.1587 21 1550 0 -3430 3430 0.1879 0.1887 0.1887 0.1587 22 1600 0 -3430 3430 0.1803 0.1879 0.1879 0.1587 23 1600 0 -3430 3430 0.1803 0.1803 0.1803 0.1587 24 1500 0 -3430 3430 0.1957 0.1803 0.1803 0.1587 25 1550 0 -3430 3430 0.1879 0.1957 0.1957 0.1587

\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[1]) \tabularnewline
0 &  & - & - & - & - & - & - & - \tabularnewline
1 & 1750 & - & - & - & - & - & - & - \tabularnewline
2 & 1800 & 0 & -3430 & 3430 & 0.1518 & 0.1587 & 0.1587 & 0.1587 \tabularnewline
3 & 1600 & 0 & -3430 & 3430 & 0.1803 & 0.1518 & 0.1518 & 0.1587 \tabularnewline
4 & 1750 & 0 & -3430 & 3430 & 0.1587 & 0.1803 & 0.1803 & 0.1587 \tabularnewline
5 & 2100 & 0 & -3430 & 3430 & 0.1151 & 0.1587 & 0.1587 & 0.1587 \tabularnewline
6 & 1900 & 0 & -3430 & 3430 & 0.1388 & 0.1151 & 0.1151 & 0.1587 \tabularnewline
7 & 1500 & 0 & -3430 & 3430 & 0.1957 & 0.1388 & 0.1388 & 0.1587 \tabularnewline
8 & 1700 & 0 & -3430 & 3430 & 0.1657 & 0.1957 & 0.1957 & 0.1587 \tabularnewline
9 & 2500 & 0 & -3430 & 3430 & 0.0766 & 0.1657 & 0.1657 & 0.1587 \tabularnewline
10 & 1800 & 0 & -3430 & 3430 & 0.1518 & 0.0766 & 0.0766 & 0.1587 \tabularnewline
11 & 1600 & 0 & -3430 & 3430 & 0.1803 & 0.1518 & 0.1518 & 0.1587 \tabularnewline
12 & 1500 & 0 & -3430 & 3430 & 0.1957 & 0.1803 & 0.1803 & 0.1587 \tabularnewline
13 & 1500 & 0 & -3430 & 3430 & 0.1957 & 0.1957 & 0.1957 & 0.1587 \tabularnewline
14 & 1450 & 0 & -3430 & 3430 & 0.2037 & 0.1957 & 0.1957 & 0.1587 \tabularnewline
15 & 1450 & 0 & -3430 & 3430 & 0.2037 & 0.2037 & 0.2037 & 0.1587 \tabularnewline
16 & 1600 & 0 & -3430 & 3430 & 0.1803 & 0.2037 & 0.2037 & 0.1587 \tabularnewline
17 & 1800 & 0 & -3430 & 3430 & 0.1518 & 0.1803 & 0.1803 & 0.1587 \tabularnewline
18 & 1500 & 0 & -3430 & 3430 & 0.1957 & 0.1518 & 0.1518 & 0.1587 \tabularnewline
19 & 1500 & 0 & -3430 & 3430 & 0.1957 & 0.1957 & 0.1957 & 0.1587 \tabularnewline
20 & 1545 & 0 & -3430 & 3430 & 0.1887 & 0.1957 & 0.1957 & 0.1587 \tabularnewline
21 & 1550 & 0 & -3430 & 3430 & 0.1879 & 0.1887 & 0.1887 & 0.1587 \tabularnewline
22 & 1600 & 0 & -3430 & 3430 & 0.1803 & 0.1879 & 0.1879 & 0.1587 \tabularnewline
23 & 1600 & 0 & -3430 & 3430 & 0.1803 & 0.1803 & 0.1803 & 0.1587 \tabularnewline
24 & 1500 & 0 & -3430 & 3430 & 0.1957 & 0.1803 & 0.1803 & 0.1587 \tabularnewline
25 & 1550 & 0 & -3430 & 3430 & 0.1879 & 0.1957 & 0.1957 & 0.1587 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232258&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[1])[/C][/ROW]
[ROW][C]0[/C][C][/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]1[/C][C]1750[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][C]-[/C][/ROW]
[ROW][C]2[/C][C]1800[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1518[/C][C]0.1587[/C][C]0.1587[/C][C]0.1587[/C][/ROW]
[ROW][C]3[/C][C]1600[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1803[/C][C]0.1518[/C][C]0.1518[/C][C]0.1587[/C][/ROW]
[ROW][C]4[/C][C]1750[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1587[/C][C]0.1803[/C][C]0.1803[/C][C]0.1587[/C][/ROW]
[ROW][C]5[/C][C]2100[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1151[/C][C]0.1587[/C][C]0.1587[/C][C]0.1587[/C][/ROW]
[ROW][C]6[/C][C]1900[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1388[/C][C]0.1151[/C][C]0.1151[/C][C]0.1587[/C][/ROW]
[ROW][C]7[/C][C]1500[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1957[/C][C]0.1388[/C][C]0.1388[/C][C]0.1587[/C][/ROW]
[ROW][C]8[/C][C]1700[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1657[/C][C]0.1957[/C][C]0.1957[/C][C]0.1587[/C][/ROW]
[ROW][C]9[/C][C]2500[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.0766[/C][C]0.1657[/C][C]0.1657[/C][C]0.1587[/C][/ROW]
[ROW][C]10[/C][C]1800[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1518[/C][C]0.0766[/C][C]0.0766[/C][C]0.1587[/C][/ROW]
[ROW][C]11[/C][C]1600[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1803[/C][C]0.1518[/C][C]0.1518[/C][C]0.1587[/C][/ROW]
[ROW][C]12[/C][C]1500[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1957[/C][C]0.1803[/C][C]0.1803[/C][C]0.1587[/C][/ROW]
[ROW][C]13[/C][C]1500[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1957[/C][C]0.1957[/C][C]0.1957[/C][C]0.1587[/C][/ROW]
[ROW][C]14[/C][C]1450[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.2037[/C][C]0.1957[/C][C]0.1957[/C][C]0.1587[/C][/ROW]
[ROW][C]15[/C][C]1450[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.2037[/C][C]0.2037[/C][C]0.2037[/C][C]0.1587[/C][/ROW]
[ROW][C]16[/C][C]1600[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1803[/C][C]0.2037[/C][C]0.2037[/C][C]0.1587[/C][/ROW]
[ROW][C]17[/C][C]1800[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1518[/C][C]0.1803[/C][C]0.1803[/C][C]0.1587[/C][/ROW]
[ROW][C]18[/C][C]1500[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1957[/C][C]0.1518[/C][C]0.1518[/C][C]0.1587[/C][/ROW]
[ROW][C]19[/C][C]1500[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1957[/C][C]0.1957[/C][C]0.1957[/C][C]0.1587[/C][/ROW]
[ROW][C]20[/C][C]1545[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1887[/C][C]0.1957[/C][C]0.1957[/C][C]0.1587[/C][/ROW]
[ROW][C]21[/C][C]1550[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1879[/C][C]0.1887[/C][C]0.1887[/C][C]0.1587[/C][/ROW]
[ROW][C]22[/C][C]1600[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1803[/C][C]0.1879[/C][C]0.1879[/C][C]0.1587[/C][/ROW]
[ROW][C]23[/C][C]1600[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1803[/C][C]0.1803[/C][C]0.1803[/C][C]0.1587[/C][/ROW]
[ROW][C]24[/C][C]1500[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1957[/C][C]0.1803[/C][C]0.1803[/C][C]0.1587[/C][/ROW]
[ROW][C]25[/C][C]1550[/C][C]0[/C][C]-3430[/C][C]3430[/C][C]0.1879[/C][C]0.1957[/C][C]0.1957[/C][C]0.1587[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232258&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232258&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 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[1]) 0 - - - - - - - 1 1750 - - - - - - - 2 1800 0 -3430 3430 0.1518 0.1587 0.1587 0.1587 3 1600 0 -3430 3430 0.1803 0.1518 0.1518 0.1587 4 1750 0 -3430 3430 0.1587 0.1803 0.1803 0.1587 5 2100 0 -3430 3430 0.1151 0.1587 0.1587 0.1587 6 1900 0 -3430 3430 0.1388 0.1151 0.1151 0.1587 7 1500 0 -3430 3430 0.1957 0.1388 0.1388 0.1587 8 1700 0 -3430 3430 0.1657 0.1957 0.1957 0.1587 9 2500 0 -3430 3430 0.0766 0.1657 0.1657 0.1587 10 1800 0 -3430 3430 0.1518 0.0766 0.0766 0.1587 11 1600 0 -3430 3430 0.1803 0.1518 0.1518 0.1587 12 1500 0 -3430 3430 0.1957 0.1803 0.1803 0.1587 13 1500 0 -3430 3430 0.1957 0.1957 0.1957 0.1587 14 1450 0 -3430 3430 0.2037 0.1957 0.1957 0.1587 15 1450 0 -3430 3430 0.2037 0.2037 0.2037 0.1587 16 1600 0 -3430 3430 0.1803 0.2037 0.2037 0.1587 17 1800 0 -3430 3430 0.1518 0.1803 0.1803 0.1587 18 1500 0 -3430 3430 0.1957 0.1518 0.1518 0.1587 19 1500 0 -3430 3430 0.1957 0.1957 0.1957 0.1587 20 1545 0 -3430 3430 0.1887 0.1957 0.1957 0.1587 21 1550 0 -3430 3430 0.1879 0.1887 0.1887 0.1587 22 1600 0 -3430 3430 0.1803 0.1879 0.1879 0.1587 23 1600 0 -3430 3430 0.1803 0.1803 0.1803 0.1587 24 1500 0 -3430 3430 0.1957 0.1803 0.1803 0.1587 25 1550 0 -3430 3430 0.1879 0.1957 0.1957 0.1587

 Univariate ARIMA Extrapolation Forecast Performance time % S.E. PE MAPE sMAPE Sq.E MSE RMSE ScaledE MASE 2 Inf 1 1 2 3240000 0 0 9.7412 9.7412 3 Inf 1 1 2 2560000 2900000 1702.9386 8.6588 9.2 4 Inf 1 1 2 3062500 2954166.6667 1718.7689 9.4706 9.2902 5 Inf 1 1 2 4410000 3318125 1821.5721 11.3647 9.8088 6 Inf 1 1 2 3610000 3376500 1837.5255 10.2824 9.9035 7 Inf 1 1 2 2250000 3188750 1785.7071 8.1176 9.6059 8 Inf 1 1 2 2890000 3146071.4286 1773.7168 9.2 9.5479 9 Inf 1 1 2 6250000 3534062.5 1879.9102 13.5294 10.0456 10 Inf 1 1 2 3240000 3501388.8889 1871.1999 9.7412 10.0118 11 Inf 1 1 2 2560000 3407250 1845.8738 8.6588 9.8765 12 Inf 1 1 2 2250000 3302045.4545 1817.1531 8.1176 9.7166 13 Inf 1 1 2 2250000 3214375 1792.8678 8.1176 9.5833 14 Inf 1 1 2 2102500 3128846.1538 1768.8545 7.8471 9.4498 15 Inf 1 1 2 2102500 3055535.7143 1748.0091 7.8471 9.3353 16 Inf 1 1 2 2560000 3022500 1738.5339 8.6588 9.2902 17 Inf 1 1 2 3240000 3036093.75 1742.439 9.7412 9.3184 18 Inf 1 1 2 2250000 2989852.9412 1729.1191 8.1176 9.2478 19 Inf 1 1 2 2250000 2948750 1717.1925 8.1176 9.185 20 Inf 1 1 2 2387025 2919185.5263 1708.5624 8.3612 9.1416 21 Inf 1 1 2 2402500 2893351.25 1700.9854 8.3882 9.1039 22 Inf 1 1 2 2560000 2877477.381 1696.3129 8.6588 9.0827 23 Inf 1 1 2 2560000 2863046.5909 1692.054 8.6588 9.0635 24 Inf 1 1 2 2250000 2836392.3913 1684.1593 8.1176 9.0224 25 Inf 1 1 2 2402500 2818313.5417 1678.7834 8.3882 8.9959

\begin{tabular}{lllllllll}
\hline
Univariate ARIMA Extrapolation Forecast Performance \tabularnewline
time & % S.E. & PE & MAPE & sMAPE & Sq.E & MSE & RMSE & ScaledE & MASE \tabularnewline
2 & Inf & 1 & 1 & 2 & 3240000 & 0 & 0 & 9.7412 & 9.7412 \tabularnewline
3 & Inf & 1 & 1 & 2 & 2560000 & 2900000 & 1702.9386 & 8.6588 & 9.2 \tabularnewline
4 & Inf & 1 & 1 & 2 & 3062500 & 2954166.6667 & 1718.7689 & 9.4706 & 9.2902 \tabularnewline
5 & Inf & 1 & 1 & 2 & 4410000 & 3318125 & 1821.5721 & 11.3647 & 9.8088 \tabularnewline
6 & Inf & 1 & 1 & 2 & 3610000 & 3376500 & 1837.5255 & 10.2824 & 9.9035 \tabularnewline
7 & Inf & 1 & 1 & 2 & 2250000 & 3188750 & 1785.7071 & 8.1176 & 9.6059 \tabularnewline
8 & Inf & 1 & 1 & 2 & 2890000 & 3146071.4286 & 1773.7168 & 9.2 & 9.5479 \tabularnewline
9 & Inf & 1 & 1 & 2 & 6250000 & 3534062.5 & 1879.9102 & 13.5294 & 10.0456 \tabularnewline
10 & Inf & 1 & 1 & 2 & 3240000 & 3501388.8889 & 1871.1999 & 9.7412 & 10.0118 \tabularnewline
11 & Inf & 1 & 1 & 2 & 2560000 & 3407250 & 1845.8738 & 8.6588 & 9.8765 \tabularnewline
12 & Inf & 1 & 1 & 2 & 2250000 & 3302045.4545 & 1817.1531 & 8.1176 & 9.7166 \tabularnewline
13 & Inf & 1 & 1 & 2 & 2250000 & 3214375 & 1792.8678 & 8.1176 & 9.5833 \tabularnewline
14 & Inf & 1 & 1 & 2 & 2102500 & 3128846.1538 & 1768.8545 & 7.8471 & 9.4498 \tabularnewline
15 & Inf & 1 & 1 & 2 & 2102500 & 3055535.7143 & 1748.0091 & 7.8471 & 9.3353 \tabularnewline
16 & Inf & 1 & 1 & 2 & 2560000 & 3022500 & 1738.5339 & 8.6588 & 9.2902 \tabularnewline
17 & Inf & 1 & 1 & 2 & 3240000 & 3036093.75 & 1742.439 & 9.7412 & 9.3184 \tabularnewline
18 & Inf & 1 & 1 & 2 & 2250000 & 2989852.9412 & 1729.1191 & 8.1176 & 9.2478 \tabularnewline
19 & Inf & 1 & 1 & 2 & 2250000 & 2948750 & 1717.1925 & 8.1176 & 9.185 \tabularnewline
20 & Inf & 1 & 1 & 2 & 2387025 & 2919185.5263 & 1708.5624 & 8.3612 & 9.1416 \tabularnewline
21 & Inf & 1 & 1 & 2 & 2402500 & 2893351.25 & 1700.9854 & 8.3882 & 9.1039 \tabularnewline
22 & Inf & 1 & 1 & 2 & 2560000 & 2877477.381 & 1696.3129 & 8.6588 & 9.0827 \tabularnewline
23 & Inf & 1 & 1 & 2 & 2560000 & 2863046.5909 & 1692.054 & 8.6588 & 9.0635 \tabularnewline
24 & Inf & 1 & 1 & 2 & 2250000 & 2836392.3913 & 1684.1593 & 8.1176 & 9.0224 \tabularnewline
25 & Inf & 1 & 1 & 2 & 2402500 & 2818313.5417 & 1678.7834 & 8.3882 & 8.9959 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232258&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]sMAPE[/C][C]Sq.E[/C][C]MSE[/C][C]RMSE[/C][C]ScaledE[/C][C]MASE[/C][/ROW]
[ROW][C]2[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]3240000[/C][C]0[/C][C]0[/C][C]9.7412[/C][C]9.7412[/C][/ROW]
[ROW][C]3[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2560000[/C][C]2900000[/C][C]1702.9386[/C][C]8.6588[/C][C]9.2[/C][/ROW]
[ROW][C]4[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]3062500[/C][C]2954166.6667[/C][C]1718.7689[/C][C]9.4706[/C][C]9.2902[/C][/ROW]
[ROW][C]5[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]4410000[/C][C]3318125[/C][C]1821.5721[/C][C]11.3647[/C][C]9.8088[/C][/ROW]
[ROW][C]6[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]3610000[/C][C]3376500[/C][C]1837.5255[/C][C]10.2824[/C][C]9.9035[/C][/ROW]
[ROW][C]7[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2250000[/C][C]3188750[/C][C]1785.7071[/C][C]8.1176[/C][C]9.6059[/C][/ROW]
[ROW][C]8[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2890000[/C][C]3146071.4286[/C][C]1773.7168[/C][C]9.2[/C][C]9.5479[/C][/ROW]
[ROW][C]9[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]6250000[/C][C]3534062.5[/C][C]1879.9102[/C][C]13.5294[/C][C]10.0456[/C][/ROW]
[ROW][C]10[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]3240000[/C][C]3501388.8889[/C][C]1871.1999[/C][C]9.7412[/C][C]10.0118[/C][/ROW]
[ROW][C]11[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2560000[/C][C]3407250[/C][C]1845.8738[/C][C]8.6588[/C][C]9.8765[/C][/ROW]
[ROW][C]12[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2250000[/C][C]3302045.4545[/C][C]1817.1531[/C][C]8.1176[/C][C]9.7166[/C][/ROW]
[ROW][C]13[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2250000[/C][C]3214375[/C][C]1792.8678[/C][C]8.1176[/C][C]9.5833[/C][/ROW]
[ROW][C]14[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2102500[/C][C]3128846.1538[/C][C]1768.8545[/C][C]7.8471[/C][C]9.4498[/C][/ROW]
[ROW][C]15[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2102500[/C][C]3055535.7143[/C][C]1748.0091[/C][C]7.8471[/C][C]9.3353[/C][/ROW]
[ROW][C]16[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2560000[/C][C]3022500[/C][C]1738.5339[/C][C]8.6588[/C][C]9.2902[/C][/ROW]
[ROW][C]17[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]3240000[/C][C]3036093.75[/C][C]1742.439[/C][C]9.7412[/C][C]9.3184[/C][/ROW]
[ROW][C]18[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2250000[/C][C]2989852.9412[/C][C]1729.1191[/C][C]8.1176[/C][C]9.2478[/C][/ROW]
[ROW][C]19[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2250000[/C][C]2948750[/C][C]1717.1925[/C][C]8.1176[/C][C]9.185[/C][/ROW]
[ROW][C]20[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2387025[/C][C]2919185.5263[/C][C]1708.5624[/C][C]8.3612[/C][C]9.1416[/C][/ROW]
[ROW][C]21[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2402500[/C][C]2893351.25[/C][C]1700.9854[/C][C]8.3882[/C][C]9.1039[/C][/ROW]
[ROW][C]22[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2560000[/C][C]2877477.381[/C][C]1696.3129[/C][C]8.6588[/C][C]9.0827[/C][/ROW]
[ROW][C]23[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2560000[/C][C]2863046.5909[/C][C]1692.054[/C][C]8.6588[/C][C]9.0635[/C][/ROW]
[ROW][C]24[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2250000[/C][C]2836392.3913[/C][C]1684.1593[/C][C]8.1176[/C][C]9.0224[/C][/ROW]
[ROW][C]25[/C][C]Inf[/C][C]1[/C][C]1[/C][C]2[/C][C]2402500[/C][C]2818313.5417[/C][C]1678.7834[/C][C]8.3882[/C][C]8.9959[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232258&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232258&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. PE MAPE sMAPE Sq.E MSE RMSE ScaledE MASE 2 Inf 1 1 2 3240000 0 0 9.7412 9.7412 3 Inf 1 1 2 2560000 2900000 1702.9386 8.6588 9.2 4 Inf 1 1 2 3062500 2954166.6667 1718.7689 9.4706 9.2902 5 Inf 1 1 2 4410000 3318125 1821.5721 11.3647 9.8088 6 Inf 1 1 2 3610000 3376500 1837.5255 10.2824 9.9035 7 Inf 1 1 2 2250000 3188750 1785.7071 8.1176 9.6059 8 Inf 1 1 2 2890000 3146071.4286 1773.7168 9.2 9.5479 9 Inf 1 1 2 6250000 3534062.5 1879.9102 13.5294 10.0456 10 Inf 1 1 2 3240000 3501388.8889 1871.1999 9.7412 10.0118 11 Inf 1 1 2 2560000 3407250 1845.8738 8.6588 9.8765 12 Inf 1 1 2 2250000 3302045.4545 1817.1531 8.1176 9.7166 13 Inf 1 1 2 2250000 3214375 1792.8678 8.1176 9.5833 14 Inf 1 1 2 2102500 3128846.1538 1768.8545 7.8471 9.4498 15 Inf 1 1 2 2102500 3055535.7143 1748.0091 7.8471 9.3353 16 Inf 1 1 2 2560000 3022500 1738.5339 8.6588 9.2902 17 Inf 1 1 2 3240000 3036093.75 1742.439 9.7412 9.3184 18 Inf 1 1 2 2250000 2989852.9412 1729.1191 8.1176 9.2478 19 Inf 1 1 2 2250000 2948750 1717.1925 8.1176 9.185 20 Inf 1 1 2 2387025 2919185.5263 1708.5624 8.3612 9.1416 21 Inf 1 1 2 2402500 2893351.25 1700.9854 8.3882 9.1039 22 Inf 1 1 2 2560000 2877477.381 1696.3129 8.6588 9.0827 23 Inf 1 1 2 2560000 2863046.5909 1692.054 8.6588 9.0635 24 Inf 1 1 2 2250000 2836392.3913 1684.1593 8.1176 9.0224 25 Inf 1 1 2 2402500 2818313.5417 1678.7834 8.3882 8.9959

 PNG link Postscript link PDF link

 PNG link Postscript link PDF link

Parameters (Session):
par1 = 24 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
Parameters (R input):
par1 = 24 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 0 ; par9 = 0 ; par10 = FALSE ;
R code (references can be found in the software module):
par10 <- 'TRUE'par9 <- '0'par8 <- '0'par7 <- '0'par6 <- '0'par5 <- '1'par4 <- '0'par3 <- '0'par2 <- '1'par1 <- '24'par1 <- as.numeric(par1) #cut off periodspar2 <- as.numeric(par2) #lambdapar3 <- as.numeric(par3) #degree of non-seasonal differencingpar4 <- as.numeric(par4) #degree of seasonal differencingpar5 <- as.numeric(par5) #seasonal periodpar6 <- as.numeric(par6) #ppar7 <- as.numeric(par7) #qpar8 <- as.numeric(par8) #Ppar9 <- as.numeric(par9) #Qif (par10 == 'TRUE') par10 <- TRUEif (par10 == 'FALSE') par10 <- FALSEif (par2 == 0) x <- log(x)if (par2 != 0) x <- x^par2lx <- length(x)first <- lx - 2*par1nx <- lx - par1nx1 <- nx + 1fx <- lx - nxif (fx < 1) {fx <- par5nx1 <- lx + fx - 1first <- lx - 2*fx}first <- 1if (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 <- lblb <- ubub <- 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.spe <- array(0, dim=fx)perf.scalederr <- array(0, dim=fx)perf.mase <- array(0, dim=fx)perf.mase1 <- array(0, dim=fx)perf.mape <- array(0, dim=fx)perf.smape <- array(0, dim=fx)perf.mape1 <- array(0, dim=fx)perf.smape1 <- 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)perf.scaleddenom <- 0for (i in 2:fx) {perf.scaleddenom = perf.scaleddenom + abs(x[nx+i] - x[nx+i-1])}perf.scaleddenom = perf.scaleddenom / (fx-1)for (i in 1:fx) {locSD <- (ub[i] - forecast$pred[i]) / 1.96perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenomperf.pe[i] = (x[nx+i] - forecast$pred[i]) / x[nx+i]perf.spe[i] = 2*(x[nx+i] - forecast$pred[i]) / (x[nx+i] + forecast$pred[i])perf.se[i] = (x[nx+i] - forecast$pred[i])^2prob.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.smape[1] = abs(perf.spe[1])perf.mape1[1] = perf.mape[1]perf.smape1[1] = perf.smape[1]perf.mse[1] = perf.se[1]perf.mase[1] = abs(perf.scalederr[1])perf.mase1[1] = perf.mase[1]for (i in 2:fx) {perf.mape[i] = perf.mape[i-1] + abs(perf.pe[i])perf.mape1[i] = perf.mape[i] / iperf.smape[i] = perf.smape[i-1] + abs(perf.spe[i])perf.smape1[i] = perf.smape[i] / iperf.mse[i] = perf.mse[i-1] + perf.se[i]perf.mse1[i] = perf.mse[i] / iperf.mase[i] = perf.mase[i-1] + abs(perf.scalederr[i])perf.mase1[i] = perf.mase[i] / i}perf.rmse = sqrt(perf.mse1)bitmap(file='test2.png')plot(forecast$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))dum <- forecast$preddum[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',10,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,'sMAPE',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.element(a,'ScaledE',1,header=TRUE)a<-table.element(a,'MASE',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.smape1[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.element(a,round(perf.scalederr[i],4))a<-table.element(a,round(perf.mase1[i],4))a<-table.row.end(a)}a<-table.end(a)table.save(a,file='mytable1.tab')