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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)
-       [ARIMA Forecasting] [ARIMA navHomePage...] [2013-12-12 16:19:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ yule.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[1])
0-------
11750-------
218000-343034300.15180.15870.15870.1587
316000-343034300.18030.15180.15180.1587
417500-343034300.15870.18030.18030.1587
521000-343034300.11510.15870.15870.1587
619000-343034300.13880.11510.11510.1587
715000-343034300.19570.13880.13880.1587
817000-343034300.16570.19570.19570.1587
925000-343034300.07660.16570.16570.1587
1018000-343034300.15180.07660.07660.1587
1116000-343034300.18030.15180.15180.1587
1215000-343034300.19570.18030.18030.1587
1315000-343034300.19570.19570.19570.1587
1414500-343034300.20370.19570.19570.1587
1514500-343034300.20370.20370.20370.1587
1616000-343034300.18030.20370.20370.1587
1718000-343034300.15180.18030.18030.1587
1815000-343034300.19570.15180.15180.1587
1915000-343034300.19570.19570.19570.1587
2015450-343034300.18870.19570.19570.1587
2115500-343034300.18790.18870.18870.1587
2216000-343034300.18030.18790.18790.1587
2316000-343034300.18030.18030.18030.1587
2415000-343034300.19570.18030.18030.1587
2515500-343034300.18790.19570.19570.1587

\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
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[1])
0-------
11750-------
218000-343034300.15180.15870.15870.1587
316000-343034300.18030.15180.15180.1587
417500-343034300.15870.18030.18030.1587
521000-343034300.11510.15870.15870.1587
619000-343034300.13880.11510.11510.1587
715000-343034300.19570.13880.13880.1587
817000-343034300.16570.19570.19570.1587
925000-343034300.07660.16570.16570.1587
1018000-343034300.15180.07660.07660.1587
1116000-343034300.18030.15180.15180.1587
1215000-343034300.19570.18030.18030.1587
1315000-343034300.19570.19570.19570.1587
1414500-343034300.20370.19570.19570.1587
1514500-343034300.20370.20370.20370.1587
1616000-343034300.18030.20370.20370.1587
1718000-343034300.15180.18030.18030.1587
1815000-343034300.19570.15180.15180.1587
1915000-343034300.19570.19570.19570.1587
2015450-343034300.18870.19570.19570.1587
2115500-343034300.18790.18870.18870.1587
2216000-343034300.18030.18790.18790.1587
2316000-343034300.18030.18030.18030.1587
2415000-343034300.19570.18030.18030.1587
2515500-343034300.18790.19570.19570.1587







Univariate ARIMA Extrapolation Forecast Performance
time% S.E.PEMAPEsMAPESq.EMSERMSEScaledEMASE
2Inf1123240000009.74129.7412
3Inf112256000029000001702.93868.65889.2
4Inf11230625002954166.66671718.76899.47069.2902
5Inf112441000033181251821.572111.36479.8088
6Inf112361000033765001837.525510.28249.9035
7Inf112225000031887501785.70718.11769.6059
8Inf11228900003146071.42861773.71689.29.5479
9Inf11262500003534062.51879.910213.529410.0456
10Inf11232400003501388.88891871.19999.741210.0118
11Inf112256000034072501845.87388.65889.8765
12Inf11222500003302045.45451817.15318.11769.7166
13Inf112225000032143751792.86788.11769.5833
14Inf11221025003128846.15381768.85457.84719.4498
15Inf11221025003055535.71431748.00917.84719.3353
16Inf112256000030225001738.53398.65889.2902
17Inf11232400003036093.751742.4399.74129.3184
18Inf11222500002989852.94121729.11918.11769.2478
19Inf112225000029487501717.19258.11769.185
20Inf11223870252919185.52631708.56248.36129.1416
21Inf11224025002893351.251700.98548.38829.1039
22Inf11225600002877477.3811696.31298.65889.0827
23Inf11225600002863046.59091692.0548.65889.0635
24Inf11222500002836392.39131684.15938.11769.0224
25Inf11224025002818313.54171678.78348.38828.9959

\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.PEMAPEsMAPESq.EMSERMSEScaledEMASE
2Inf1123240000009.74129.7412
3Inf112256000029000001702.93868.65889.2
4Inf11230625002954166.66671718.76899.47069.2902
5Inf112441000033181251821.572111.36479.8088
6Inf112361000033765001837.525510.28249.9035
7Inf112225000031887501785.70718.11769.6059
8Inf11228900003146071.42861773.71689.29.5479
9Inf11262500003534062.51879.910213.529410.0456
10Inf11232400003501388.88891871.19999.741210.0118
11Inf112256000034072501845.87388.65889.8765
12Inf11222500003302045.45451817.15318.11769.7166
13Inf112225000032143751792.86788.11769.5833
14Inf11221025003128846.15381768.85457.84719.4498
15Inf11221025003055535.71431748.00917.84719.3353
16Inf112256000030225001738.53398.65889.2902
17Inf11232400003036093.751742.4399.74129.3184
18Inf11222500002989852.94121729.11918.11769.2478
19Inf112225000029487501717.19258.11769.185
20Inf11223870252919185.52631708.56248.36129.1416
21Inf11224025002893351.251700.98548.38829.1039
22Inf11225600002877477.3811696.31298.65889.0827
23Inf11225600002863046.59091692.0548.65889.0635
24Inf11222500002836392.39131684.15938.11769.0224
25Inf11224025002818313.54171678.78348.38828.9959



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 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.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 <- 0
for (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$pred[i]) / 1.96
perf.scalederr[i] = (x[nx+i] - forecast$pred[i]) / perf.scaleddenom
perf.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])^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.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] / i
perf.smape[i] = perf.smape[i-1] + abs(perf.spe[i])
perf.smape1[i] = perf.smape[i] / i
perf.mse[i] = perf.mse[i-1] + perf.se[i]
perf.mse1[i] = perf.mse[i] / i
perf.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$pred, pch=19, type='b',main='ARIMA Extrapolation Forecast', ylab='Forecast and 95% CI', xlab='time',ylim=c(min(lb),max(ub)))
dum <- forecast$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',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')